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SmartFit | build status https app travis ci com marcos toranzo smartfit svg branch main https app travis ci com marcos toranzo smartfit smartfit this project tries to facilitate the users the way they stay fit by balancing the amount and type of exercise done during the whole day not just during the short sessions dedicated specifically to the physical excercise project for the cloud computing course of the computer engineering master at university of granada getting started requirements make python3 pip3 tasks the tasks related to the project are implemented in the makefile https github com marcos toranzo smartfit blob main makefile in directory root here you can run test will run every test in the tests https github com marcos toranzo smartfit tree main tests folder uses pytest check checks for compilation and sintactic errors install install dependencies of the project container test run tests in docker container run server run the api server you can read more about the choice of task manager docs task manager md dotenv the project uses the configuration set in the env file in the root directory right now only the host and port configuarion for the api server is used this file is ignored by git since it can contain sensitive informatios like credentials and secret keys we don t want to make public in case of not having specified host the default value will be 127 0 0 1 in the case of the port it will be 8000 documentation you can read a more detailed description docs description md of the problem been solved you can consult the business logic docs business logic md for the project you can read all about the user journeys docs user journeys md where possible flows and situations are described as well as the user roles you can check the milestones docs milestones md for the project where each one defines a mvp to achieve when finished you can read the user stories docs user stories md that define the functioning of the app you can learn about the testing libraries and framework docs testing framework md used in the project and why we chose them in order to run the tests in a controlled and secure environment docker containers docs container md were used information about the continuous integration docs ci md implemented in the project you can read about the api services docs api md implemented the process of testing them and how they comply with the user stories initial configuration go here docs initial configuration md to see the steps taken for the initial configuration | cloud |
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PyTorch-Deep-Learning-Template | pytorch deep learning template a clean and simple template to kick start your next dl project francesco saverio zuppichini in this article we present you a deep learning template based on pytorch this template aims to make it easier for you to start a new deep learning computer vision project with pytorch the main features are modularity we split each logic piece into a different python submodule data augmentation we included imgaug https imgaug readthedocs io en latest ready to go by using poutyne https pypi org project poutyne a keras like framework you don t have to write any train loop torchsummary https github com sksq96 pytorch summary to show a summary of your models reduce the learning rate on a plateau auto saving the best model experiment tracking with comet https www comet ml logging using python logging https docs python org 3 library logging html module a playground notebook to quick test play around installation clone the repo and go inside it then run pip install r requirements txt motivation let s face it usually data scientists are not software engineers and they usually end up with spaghetti code most of the time on a big unusable jupiter notebook with this repo i have proposed a clean example of how your code should be split and modularized to make scalability and sharability possible in this example we will try to classify darth vader and luke skywalker we have 100 images per class gathered using google images the dataset is here https drive google com open id 1lyhjxuvjogdiggjl4mndha10xjejwuw7 you just have to extract it in this folder and run main py we are fine tuning resnet18 and it should be able to reach 90 accuracy in 5 10 epochs structure the template is inside template callbacks here you can create your custom callbacks checkpoint were we store the trained models data here we define our dataset transformation custom transformation e g resize and data augmentation dataset the data train val logger py were we define our logger losses custom losses main py models here we create our models mycnn py resnet py utils py playground ipynb a notebook that can be used to fast experiment with things project py a class that represents the project structure readme md requirements txt test you should always perform some basic testing test mydataset py utils py utilities functions we strongly encourage to play around with the template keep your structure clean and concise every deep learning project has at least three mains steps data gathering processing modeling training evaluating project one good idea is to store all the paths at an interesting location e g the dataset folder in a shared class that can be accessed by anyone in the folder you should never hardcode any paths and always define them once and import them so if you later change your structure you will only have to modify one file if we have a look at project py we can see how we defined the data dir and the checkpoint dir once for all we are using the new path https docs python org 3 library pathlib html apis that support different os out of the box and also makes it easier to join and concatenate paths alt https raw githubusercontent com francescosaveriozuppichini pytorch deep learning skeletron master images project png for example if we want to know the data location we can python3 from project import project project project print project data dir foo baa dataset data in the data package you can define your own dataset as always by subclassing torch data utils dataset exposing transformations and utilities to work with your data in our example we directly used imagedataset from torchvision but we included a skeleton for a custom dataset in data mydataset transformation you usually have to do some preprocessing on the data e g resize the images and apply data augmentation all your transformation should go inside data trasformation in our template we included a wrapper for imgaug https imgaug readthedocs io en latest alt https raw githubusercontent com francescosaveriozuppichini pytorch deep learning skeletron master images transformation png dataloaders as you know you have to create a dataloader to feed your data into the model in the data init py file we expose a very simple function get dataloaders to automatically configure the train val and test data loaders using few parameters alt https raw githubusercontent com francescosaveriozuppichini pytorch deep learning skeletron master images data png losses sometimes you may need to define your custom losses you can include them in the losses package for example alt https raw githubusercontent com francescosaveriozuppichini pytorch deep learning skeletron master images losses png metrics sometimes you may need to define your custom metrics for example alt https raw githubusercontent com francescosaveriozuppichini pytorch deep learning skeletron master images metrics png logging we included python logging https docs python org 3 library logging html module you can import and use it by python from logger import logging logging info print is for noobs models all your models go inside models in our case we have a very basic cnn and we override the resnet18 function to provide a frozen model to finetune alt https github com francescosaveriozuppichini pytorch deep learning skeletron blob master images resnet png raw true train evaluation in our case we kept things simple all the training and evaluation logic is inside main py where we used poutyne https pypi org project poutyne as the main library we already defined a useful list of callbacks learning rate scheduler auto save of the best model early stopping usually this is all you need alt https github com francescosaveriozuppichini pytorch deep learning skeletron blob master images main png raw true callbacks you may need to create custom callbacks with poutyne https pypi org project poutyne is very easy since it support keras like api you custom callbacks should go inside callbacks for example we have created one to update comet every epoch alt https github com francescosaveriozuppichini pytorch deep learning skeletron blob master images cometcallback png raw true track your experiment we are using comet https www comet ml to automatically track our models results this is what comet s board looks like after a few models run alt https github com francescosaveriozuppichini pytorch deep learning skeletron blob master images comet jpg raw true running main py produces the following output alt https github com francescosaveriozuppichini pytorch deep learning skeletron blob master images output jpg raw true utils we also created different utilities function to plot both dataset and dataloader they are in utils py for example calling show dl on our train and val dataset produces the following outputs alt https github com francescosaveriozuppichini pytorch deep learning skeletron blob master images figure 1 png raw true alt https github com francescosaveriozuppichini pytorch deep learning skeletron blob master images figure 2 png raw true as you can see data augmentation is correctly applied on the train set conclusions i hope you found some useful information and hopefully it this template will help you on your next amazing project let me know if you have some ideas suggestions to improve it thank you for reading todo one example for lightning https github com williamfalcon pytorch lightning one example with fastai https www fast ai show how to setup anaconda | deep-learning pytorch python computer-vision | ai |
2022-DM121 | dm121 introdu o ao desenvolvimento html5 css3 e javascript arquivos gerados durante as aulas da disciplina dm121 | front_end |
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purecss-circular-progress-bar | purecss circular progress bar read about how this was built bottom up and a live demo at https tiagobalmeida github io posts making a pure css circular progress bar html | front_end |
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awesome-CRISPR | awesome crispr list of software websites databases papers for genome engineering including but not limited to guide design genome editing outcome screening analysis etc contributions welcome https github com davidliwei awesome crispr blob master contributing md this collection is inspired by awesome single cell https github com seandavi awesome single cell did you use any of the softwares below take a survey here https forms gle qbx7mkjm7u6jssr4a contents guide design tools off target prediction algorithms genome editing outcomes and predictions screening analysis algorithms databases crispr identification and diversity reviews summary not a complete list summary imgs slide1 jpg summary imgs slide2 jpg summary imgs slide3 jpg references 1 link https labs biology ucsd edu zhao crispr web rgr design home frame set html 2 zhang et al cell press 4 1 2 2015 https www sciencedirect com science article pii s216225311630049x 3 li et al signal transduction and targeted therapy 5 1 2 2020 https labs biology ucsd edu zhao crispr web rgr design home frame set html 4 leenay et al molecular cell 62 1 137 147 2016 https www cell com molecular cell pdf s1097 2765 16 00175 1 pdf 5 yan et al prokaryotic immunity 363 88 91 2019 https science sciencemag org content sci 363 6422 88 full pdf guide design atum https www atum bio ecommerce cas9 input webserver a website to design grna s which efficiently engineer your target and minimize off target effects using atum scoring algorithms be dict http www be dict org python webserver an attention based deep learning algorithm capable of predicting base editing outcomes it is aimed to assist scientists in designing base editor experiments benchling https benchling com crispr webserver a website that can design optimal crispr grnas by analyzing target location specificity and efficiency breaking cas http bioinfogp cnb csic es tools breakingcas webserver a website of designing grnas based on multiple organisms cas designer http www rgenome net cas designer webserver a bulge allowed quick guide rna designer for crispr cas derived rgens cas13design https cas13design nygenome org webserver this resource provides optimized guide rnas to target transcripts in the human transcriptome model organisms and viral rna genomes cgat https cgat readthedocs io en latest cgat html python cgat provides an extensive suite of tools designed to assist in the analysis of genome scale data from a range of standard file formats chopchopv3 https chopchop cbu uib no webserver a web tool for selecting target sites for crispr cas9 crispr cpf1 crispr cas13 or nickase talen directed mutagenesis cld https github com boutroslab cld software crispr library designer cld a software for multispecies design of single guide rna libraries crackling https github com bmds lab crackling software crackling is a standalone pipeline that combines multiple scoring approaches and constant time search algorithms to select safe and efficient crispr cas9 guide rna from whole genomes crisflash https github com crisflash crisflash software a software to generate crispr guide rnas against genomes annotated with individual variation crispeta http crispeta crg eu python webserver a flexible tool to design optimal pairs of sgrnas for deletion of desired genomic regions crispick https portals broadinstitute org gppx crispick public webserver crispick developed by the broad institute ranks and picks candidate crisprko a i sgrna sequences to maximize on target activity for target s provided crispor http crispor tefor net webserver a program helps to design evaluate and clone guide sequences for the crispr cas9 system crisprbase https github com jfortin1 crisprbase r base functions and classes for crispr grna design for the bioconductor project crisprbowtie https github com jfortin1 crisprbowtie r a bioconductor package for on and off target alignment of crispr guide rnas with bowtie crisprbwa https github com jfortin1 crisprbwa r a bioconductor package for on and off target alignment of crispr guide rnas with bwa crisprdesign https github com jfortin1 crisprdesign r a bioconductor package for comprehensive design of crispr grnas across nucleases and base editors crispr library designer https github com boutroslab cld docker software a software for the multispecies design of sgrna libraries crispr lifepipe https www lifeandsoft com crisprlifepipe webserver a web application which allows designing grna and donor dna sequences for crispr experiments crispr multitargeter http www multicrispr net index html webserver a web based tool which automatically searches for crispr guide rna targets it can find highly similar or identical target sites in multiple genes transcripts or design targets unique to particular genes or transcripts crispr do http cistrome org crispr webserver a web application for designing and optimizing of guide sequences that target both coding and non coding regions in spcas9 crispr system across human mouse zebrafish fly and worm genomes crispr dt http bioinfolab miamioh edu crispr dt webserver a web application that allows a user to upload dna sequences set specifications according to experimental goals and recieve target candidates crispr era http crispr era stanford edu webserver a fast and comprehensive guide rna design tool for genome editing repression and activation crispr focus http cistrome org crispr focus webserver a web based platform to search and prioritize sgrnas for crispr screening experiments crispr ko https portals broadinstitute org gpp public analysis tools sgrna design webserver a tool ranks and picks sgrna sequences candidates for the targets provided while attempting to maximize on target activity and minimize off target activity crispr local http crispr hzau edu cn crispr local software webserver a local single guide rna sgrna design tool for non reference plant genomes crispr p http crispr hzau edu cn crispr2 webserver one of the most popular tools for sgrna design in plants with minimal off target effects crispr plant https www genome arizona edu crispr webserver this tool provides a platform to help researchers to design and construct specific grnas for crispr cas9 mediated genome editing in plants crispr rt http bioinfolab miamioh edu crispr rt webserver a web application for rna targeting prediction and visualization with the crispr c2c2 cas13a system crispra i https portals broadinstitute org gpp public analysis tools sgrna design crisprai webserver this tool ranks and picks crispra i sgrna sequences candidates by the gene targets provided while attempting to maximize on target activity and minimizing off target activity crisprdirect http crispr dbcls jp webserver an algorithm for on target sgrna design crisprscan http www crisprscan org webserver a novel algorithm to predict grna efficiency crisprscore https github com jfortin1 crisprscore r a bioconductor package for on and off target scoring of guide rnas crisprseek https bioconductor org packages release bioc html crisprseek html r a bioconductor package for identifying target specific guide rnas for crispr cas9 genome editing systems crisprverse https github com crisprverse r a comprehensive bioconductor ecosystem for the design of crispr guide rnas across nucleases and technologies crispy web https crispy secondarymetabolites org input webserver this tool allows researchers to interactively select a region of their genome of interest to scan for possible sgrnas crop it http cheetah bioch virginia edu adlilab crop it homepage html webserver a web tool assists biologists in designing crispr cas9 sgrnas by predicting the off target sites and ranking them according to the chances of occurrence ct finder http bioinfolab miamioh edu ct finder webserver a web service that allows a user to upload dna sequences set specifications according to experimental goals and receive candidate guide rna targets deepcas13 http deepcas13 weililab org webserver a deep learning method to predict the efficiency of cas13d casrx rna knockdown system deepcpf1 http deepcrispr info python webserver deep learning based prediction of crispr cpf1 activity at endogenous sites deepcrispr https github com bm2 lab deepcrispr python webserver a deep learning based prediction model for sgrna on target knockout efficacy and genome wide off target cleavage profile prediction deephf http www deephf com webserver optimized crispr guide rna design for two high fidelity cas9 variants by deep learning deepspcas9 http deepcrispr info deepspcas9 python webserver a deep learning based computational model for spcas9 activity prediction drsc http www flyrnai org crispr webserver this tool provides reagents targeting individual genes focused libraries genome scale libraries and other resources for on site screening e crisp http www e crisp org e crisp webserver an algorithm is available for twelve organisms and can be easily extended to design both sgrna and pgrna eupagdt http grna ctegd uga edu webserver a web tool tailored to design crispr guide rnas for eukaryotic pathogens flashfry https github com mckennalab flashfry software a command line tool for high throughput design and screening of cas9 cas12a cpf1 and other crispr targets with a focus on speed many design metrics are available including common on and off target scores gpp azimuth https portals broadinstitute org gpp public analysis tools sgrna design webserver a tool that ranks and picks candidate crisprko sgrna sequences for the targets provided while attempting to maximize on target activity and minimizing off target activity replaced by crispick gpp web portal https portals broadinstitute org gpp public webserver a web based platform for generating matching sgrna knockout crisprko designed for a mouse or human gene transcript or target sequence guide picker https www deskgen com guidebook webserver a meta tool for designing crispr experiments by presenting ten different guide rnas scoring functions in one simple graphical interface guidehom https colab research google com drive 1vjkhqeqw1okmg0vjoxcpml7ltnkvtx9e usp sharing google colab an algorithm to search and analyze on target and off target cleavage efficiency of grnas with additional information on prediction variance uncertainty aware and interpretable evaluation of cas9 grna and cas12a grna specificity for fully matched and partially mismatched targets with deep kernel learning guides http guides sanjanalab org webserrver a web application to design customized crispr knockout libraries as easily as possible without sacrificing control guidescan http www guidescan com webserver a generalized crispr guiderna design tool horizon discovery https dharmacon horizondiscovery com gene editing crispr cas9 crispr design tool webserver it provides an intuitive one stop location for guide rna design and ordering use the design tool to order guide rnas for targeted gene knockout or hdr mediated genome editing idt https www idtdna com site order designtool index crispr custom webserver it can generate crispr cas9 guide rnas targeting any sequence from any species off spotter https cm jefferson edu off spotter webserver a website identifies all genomic instances for the given combination of grna s pam number of mismatches and seed pavooc https pavooc me webserver a web tool that design and control cutting edge scored sgrnas in the blink of an eye pgrnadesign https bitbucket org liulab pgrnadesign git python an algorithm to design paired grnas for knocking out long non coding rnas lncrnas pgrnafinder https github com xiexiaowei pgrnafinder python a web based tool to design distance independent paired grna primedesign http primedesign pinellolab org webserver software a flexible and comprehensive design tool for prime editing primeedit https primeedit nygenome org webserver this website designs pegrnas and secondary sgrnas for pe2 pe3 and pe3b prime editors for clinvar human pathogenic variants protospacer workbench http www protospacer com software protospacer workbench offers an interface for finding evaluating and sharing cas9 guide rna grna designs sgrna scorerv2 0 https sgrnascorer cancer gov python webserver a software allows users to identify sgrna sites for any pam sequence of interest ssc http cistrome org ssc webserver a sequence model for predicting sgrna efficiency in crispr cas9 knockout experiments ssfinder https code google com archive p ssfinder software a high throughput crispr cas target sites prediction tool synthego https www synthego com products bioinformatics crispr design tool webserver a software chooses from over 120 000 genomes and over 8 300 species to easily design guide rnas for gene knockout with minimal off target effects wu crispr http crispr wustl edu webserver a web tool to design grna for crispr cas9 knockout system off target prediction algorithms casfinder http arep med harvard edu casfinder python an algorithm for identifying specific cas9 targets in genomes casot http casot cbi pku edu cn webserver a tool to find potential off target sites in any given genome or user provided sequence with user specified types of the protospacer adjacent motif and the number of mismatches allowed in the seed and non seed regions cas offinder http www rgenome net cas offinder webserver an algorithm that searches for potential off target sites of cas9 rna guided endonucleases cctop https crispr cos uni heidelberg de webserver an algorithm to predict crispr cas9 target chopchop http chopchop cbu uib no index php webserver a web tool for selecting target sites for crispr cas9 crispr cpf1 crispr rgen tools http www rgenome net webserver an algorithm can identify of rgen off target sites without limitation by the number of mismatches and allow variations in pam sequences recognized by cas9 meanwhile it can search for rgen targets with low potential off target effects and high knock out efficiencies in the exon region crisprtarget http bioanalysis otago ac nz crisprtarget crispr analysis html webserver a tool to explore the targets of crispr rnas flycrispr http targetfinder flycrispr neuro brown edu webserver specificity for drosophila to find crispr target sites and evaluate each identified crispr target geneious crispr site finder https www geneious com academic software it searches for off target binding sites against a database of sequences gt scan https gt scan csiro au webserver a flexible web based tool that ranks all potential targets in a user selected region of a genome in terms of how many off targets they have gt scan2 https github com bauerlab gt scan2 r it is chromatin and transcription aware target site optimization tool for crispr cas9 sgrnacas9 http www biootools com software a software package that can be applied to search rapidly for crispr target sites and analyze the potential off target cleavage sites of crispr cas9 simultaneously wge https www sanger ac uk htgt wge webserver a algorithm shows crispr sites paired or single in and around genes and scores the pairs for potential off target sites and browse individual and paired crispr sites across human mouse genome editing outcomes and predictions amplicondivider https github com mlafave amplicondivider perl amplicondivider contains the scripts used to identify deletion and insertion variants divs in dna amplicons batch ge https github com woutersteyaert batch ge git perl a batch analysis of next generation sequencing data for genome editing assessment cas analyze http www rgenome net cas analyzer webserver an online tool for assessing genome editing results using ngs data cris py https github com patrickc01 cris py python a versatile and high throughput analysis program for crispr based genome editing crispr dart https github com bimsbbioinfo crispr dart python r a workflow to analyze crispr cas induced indel mutations in targeted amplicon dna sequencing can work with single multiplexed sgrnas per region s crispr dav https github com pinetree1 crispr dav git perl r a crispr ngs data analysis and visualization pipeline crispr ga http crispr ga net webserver a platform to assess the quality of gene editing using ngs data to quantify and characterize insertions deletions and homologous recombination crispresso2 http crispresso2 pinellolab org python webserver a software pipeline for the analysis of targeted crispr cas9 sequencing data this algorithm allows for the quantification of both non homologous ends joining nhej and homologous directed repair hdr occurrences crisprmatch https github com zhangtaolab crisprmatch python an automatic calculation and visualization tool for high throughput crispr genome editing data analysis crisprvariants https github com markrobinsonuzh crisprvariants r a r based toolkit for counting localizing and plotting targeted insertion and deletion variant combinations from crispr cas9 mutagenesis experiments forecast https partslab sanger ac uk forecast python webserver a method to predict and view mutational profiles for individual grnas indelphi https www crisprindelphi design webserver a computational model that predicts the heterogeneous 100 unique mixture of indels resulting from microhomology mediated end joining mmej and non homologous end joining nhej at a crispr induced cut indelphi synthesizes known biological mechanisms of dna repair with machine learning and achieves strong accuracy lindel https lindel gs washington edu lindel webserver a logistic regression model for accurate indel prediction induced by cas9 cleavage nar 2019 https academic oup com nar advance article doi 10 1093 nar gkz487 5511473 microhomology predictor http www rgenome net mich calculator webserver a web tool can simply predict the mutation patterns caused by microhomology mediated end joining mmej and estimate how frequently unwanted in frame deletions would happen sprout https zou group github io sprout webserver a machine learning algorithm to predict the repair outcome of a crispr cas9 knockout experiment trained in primary human t cells sprout may facilitate design of spcas9 guide rnas in therapeutically important primary human cells tide tider https tide nki nl webserver quantifying non templated and templated crispr cas9 editing results from sanger sequencing screening analysis bagel https sourceforge net projects bagel for knockout screens python an algorithm is designed to identify negatively selected genes by calculating a bayes factor for each gene representing a confidence measure that the gene knockout results in a fitness defect bayesian analysis of gene knockout screens using pooled library crispr or rnai carpools https github com boutroslab carpools r a pipeline for end to end analysis of pooled crispr cas9 screening data including in depth analysis of screening quality and sgrna phenotypes castle https bitbucket org dmorgens castle python based on the empirical bayesian framework to account for multiple sources of variability including reagent efficacy and off target effects ceres https depmap org ceres r an algorithm to estimate gene dependency levels from crispr cas9 essentiality screens while accounting for this effect crisphiermix https github com timydaley crisphiermix r a hierarchical mixture model for crispr pooled screens crisprbetabinomial https cran r project org package cb2 r a software provides functions for hit gene identification and quantification of sgrna abundances for crispr pooled screen data analysis using beta binomial test crisprcloud2 http crispr nrihub org webserver a secure convenient and precise analysis pipeline for the deconvolution of your crispr pooled screening data crispr surf https github com pinellolab crispr surf webserver a computational framework to discover regulatory elements by deconvolution of crispr tiling screen data drugz https github com hart lab drugz python drugz is a software that detects synergistic and suppressor drug gene interactions in crispr screens paper genome medicine 2019 https genomemedicine biomedcentral com articles 10 1186 s13073 019 0665 3 edger http www bioconductor org packages release bioc html edger html r known as an rna seq differential expression analysis tool edger also provides complete analysis solution for screening data encore https www helmholtz muenchen de index php id 44614 java an efficient software for crispr screens identifies new players in extrinsic apoptosis gcrisprtools http bioconductor org packages release bioc vignettes gcrisprtools inst doc gcrisprtools vignette html r an r bioconductor analysis suite facilitating quality assessment target prioritization and interpretation of arbitrarily complex competitive screening experiments gscreend http bioconductor org packages gscreend r modelling asymmetric count ratios in crispr screens to decrease experiment size and improve phenotype detection paper genome biology 2020 https genomebiology biomedcentral com articles 10 1186 s13059 020 1939 1 hitselect https github com diazlab matlab a comprehensive tool for high complexity pooled screen analysis jacks https github com felicityallen jacks python a bayesian method that jointly analyses screens performed with the same guide rna library mageck vispr https bitbucket org liulab mageck vispr python a comprehensive quality control analysis and visualization workflow for crispr cas9 screens mageck https bitbucket org liulab mageck python model based analysis of genome wide crispr cas9 knockout mageck for prioritizing single guide rnas genes and hitselectpathways in genome scale crispr cas9 knockout screens paper genome biology 2014 https genomebiology biomedcentral com articles 10 1186 s13059 014 0554 4 mageckflute https bitbucket org liulab mageckflute r a pipeline for performing computational analysis of crispr screens mageckflute combines the mageck and mageck vispr algorithms and incorporates additional downstream analysis functionalities maude https github com carldeboer maude r an r package for analyzing sorting based e g facs crispr screens and other high throughput screens with a sorting readout normalisr https github com lingfeiwang normalisr python shell single cell crispr screen e g perturb seq crop seq analysis for robust efficient gene differential expression and regulatory network reconstruction with accurate fdr control paper nature communications 2021 https doi org 10 1038 s41467 021 26682 1 pbnpa https cran r project org web packages pbnpa r a permutation based non parametric analysis pbnpa algorithm which computes p values at the gene level by permuting sgrna labels and thus it avoids restrictive distributional assumptions riger https software broadinstitute org gene e extensions html gene e extension rnai gene enrichment ranking riger rsa https admin ext gnf org publications rsa perl r c redundant sirna activity rsa is a probability based approach for the analysis of large scale rnai screens scmageck https bitbucket org weililab scmageck python r a computational model to identify genes associated with multiple expression phenotypes from crispr screening coupled with single cell rna sequencing data paper genome biology 2020 https genomebiology biomedcentral com articles 10 1186 s13059 020 1928 4 screenbeam https github com jyyu screenbeam r gene level meta analysis of high throughput functional genomics rnai or crispr screens stars https portals broadinstitute org gpp public software stars python a gene ranking algorithm for genetic perturbation screens computing a score for genes using the probability mass function of a binomial distribution to analyze either shrna or sgrna based screening data databases biogrid orcs https orcs thebiogrid org webserver an open repository of crispr screens compiled through comprehensive curation efforts paper nucleic acids research 2019 https www ncbi nlm nih gov pubmed 30476227 crisp view http crispview weililab org webserver a comprehensive database of published crispr screening dataset datasets are uniformly processed using an integrated mageck vispr pipeline with quality control qc evaluations users can browse search and visualize cell lines conditions genes and associated sgrnas across datasets depmap https depmap org portal webserver a comprehensive reference map of the cancer dependency map project at the broad institute paper cell 2017 https www ncbi nlm nih gov pubmed 28753430 genomecrispr http genomecrispr dkfz de webserver a database for high throughput crispr cas9 screening experiments pickles https hartlab shinyapps io pickles webserver a database of pooled in vitro crispr knockout library essentiality screens project drive https oncologynibr shinyapps io drive webserver a compendium of cancer dependencies and synthetic lethal relationships uncovered by large scale deep rnai screening paper cell 2017 https www ncbi nlm nih gov pubmed 28753431 project score sanger depmap https score depmap sanger ac uk webserver genome scale crispr cas9 screens in 324 human cancer cell lines from 30 cancer types paper nature 2019 https www ncbi nlm nih gov pubmed 30971826 the genome targeting catalogue https www ebi ac uk gtc webserver a public repository of experiments using crispr cas enzymes manually curated from published literature encompassing both targeted and genome wide studies in 47 species crispr identification and diversity crass https ctskennerton github io crass software a program that searches through raw metagenomic reads for crispr crf http bioinfolab miamioh edu crf home php webserver software filter the invalid crisprs crisprcasdb https crisprcas i2bc paris saclay fr webserver database a database containing crispr arrays and cas genes from complete genome sequences and tools to download and query lists of repeats and spacers crisprcasfinder https crisprcas i2bc paris saclay fr software webserver a program enables the easy detection of crisprs and cas genes in user submitted sequence data crisprcastyper https typer crispr dk webserver detect crispr cas genes and arrays and predict the subtype based on both cas genes and crispr repeat sequence crisprcompar https crispr i2bc paris saclay fr crisprcompar webserver compare the crisprs of two or several genomes crisprdetect http crispr otago ac nz crisprdetect webserver software a tool to predict and analyze crispr arrays crisprdirection http bioanalysis otago ac nz crisprdirection software predict crispr orientation crisprdisco https github com crisprlab crisprdisco software identifying crispr repeat spacer arrays and cas genes in genome data sets crisprfinder https crispr i2bc paris saclay fr webserver a web tool to identify clustered regularly interspaced short palindromic repeats crisprleader http www bioinf uni freiburg de software crisprleader software a tool predicts crispr arrays in the correct orientation and annotates the crispr leader boundaries crisprmap http rna informatik uni freiburg de crisprmap input jsp webserver crisprmap provides a quick and detailed insight into repeat conservation and diversity of both bacterial and archaeal systems crisprone https omics informatics indiana edu crisprone webserver database a database provides annotation of crispr cas systems including crispr arrays of repeat spacer units and cas genes and type of predicted system s and anti repeats crisprstand http rna informatik uni freiburg de crisprmap input jsp webserver predict repeat orientations to determine the crrna encoding strand at crispr loci crt http www room220 com crt software crispr recognition tool crt a tool for automatic detection of clustered regularly interspaced palindromic repeats metacrast https github com molleraj metacrast software a tool detects crispr arrays in raw unassembled metagenomes metacrt https omics informatics indiana edu crispr software a software for de novo prediction of crispr minced https github com ctskennerton minced java a program to find clustered regularly interspaced short palindromic repeats crisprs in full genomes or environmental datasets such as assembled contigs from metagenomes patscan https www bioinformatics org wiki patscan home page webserver a pattern matcher which searches protein or nucleotide dna rna trna etc sequence archives for instances of a pattern which you input piler cr https www drive5 com pilercr software a public domain software for finding crispr repeats reviews 2018 doench am i ready for crispr a user s guide to genetic screens https www ncbi nlm nih gov pubmed 29199283 2019 esposito et al hacking the cancer genome profiling therapeutically actionable long non coding rnas using crispr cas9 screening https www cell com cancer cell fulltext s1535 6108 19 30053 4 2019 bradford et al a benchmark of computational crispr cas9 guide design methods https journals plos org ploscompbiol article id 10 1371 journal pcbi 1007274 | crispr crispr-cas9 awesome awesome-list genome-editing cas9 crispr-analysis awesome-lists | server |
LowExCalc | lowexcalc this is a microsoft excel workbook with visual basic for applications macros use it to design low exergy heating cooling by means of embedded electric cables or hydronic pipes in floors walls or ceilings it predicts both thermal comfort and energy efficiency performance it draws a cross section of the system showing materials piping cables and calculated temperatures you can test different geometries and materials from the three libraries for materials tubes cables or surface convection you can also add new products to the libraries it applies and improves upon nordtest and cen calculation methods the main imporovements encompass more accurate treatment of the thermal resistance between pipes cables and the surrounding materials and correct treatment of temperature distribution in hydronic systems in particular log mean temperature difference and exit water temperature the heat emission and 2d temperature distribution has been validated by comparison with detailed 2d fem finile element method software lisence warranty free closed source license cc by nd 4 0 https creativecommons org licenses by nd 4 0 provided with no warranty of any kind author peter schild sintef no contact the author for training documentation or product specific improvements | os |
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serialComm | serialcomm this project is designed to communicate embedded systems using serial port | os |
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Image-filter-Project | udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images your assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost | cloud |
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fewpjs-fewp-example | a front end web programming example learning goals define web programming contrast web page versus web application identify reference example liking on social media identify the three pillars of web programming introduction the phrase front end web programming is used in different ways by different people in this lesson we re going to choose a definition of web programming and show an example that demonstrates the different parts of web programming so that we know our objective for the next set of lessons what exactly does web programming mean anyway web programming at its heart is creating documents with html and styling positioning the document s content with css using javascript to provide interactivity using javascript to notify remote servers web page vs web application when a web page has a lot of javascript code the page feels closer to a computer application so some people may call it a web application however it s worth noting that there is no clear distinction between a web page and a web application for instance there s no rule like when there are three or more actions it s a web application different people draw the boundary differently more or less we call a web page an application when it feels rich identify reference example liking on social media as we move through the rest of this material we re going to use one tiny interaction as our shared or reference example web programming example favoriting a social media post regardless of the social media site instagram pinterest facebook linkedin twitter the interaction goes something like this step 1 the site renders some html content that is styled using css step 2 you see the content and decide to show your approval of it step 3 you click some visual element meant to show approval heart thumbs up 1 etc for example heart https curriculum content s3 amazonaws com fewpjs fewpjs fewp example image 30 socmediacropped png step 4 the visual element updates animates goes from empty to full jiggles etc like full heart https curriculum content s3 amazonaws com fewpjs fewpjs fewp example image 30 socmediacropped1 png step 5 behind the scenes the application tells the provider that this post has gained your approval so that the central provider can store this information and use it later for example to notify the post author that you liked their post if all goes as it should the entire interaction only takes a second or two but even this small interaction demonstrates all the concepts of front end web programming flatiron s three pillars of web programming to help us learn web programming in three distinct phases this curriculum is broken down into three essential pillars three pillars https curriculum content s3 amazonaws com fewpjs fewpjs fewp example image 31 threepillarsconcept png recognize events manipulate the dom communicate with the server in the steps of the favoriting a social media post example we italicized the characteristic verb in steps 3 5 each of those words exemplifies the activity of one of the pillars we must learn in order to make web applications recognize events https curriculum content s3 amazonaws com fewpjs fewpjs fewp example image 31 recognizeevents png step 3 showed recognizing js events your click action on the empty heart tells javascript to do work step 4 showed manipulating the dom the work javascript was told to do was to update the screen to make the heart look clicked step 5 showed communicating with the server the work javascript was told to do was to tell the social media company that you approved of this content thinking about learning these pillars are not something professional developers outside flatiron school curriculum recognize you won t go into a tech interview and be asked name the three pillars of web programming these abstractions are a way to help learners you recognize how what you re learning fits into three major activity areas that said if an interviewer asked you how to debug a web program and you said your strategy would be based on ensuring three critical areas were working we think your interview answer would be off to a good start now you know what s going on when you click that heart the next lessons will focus on explaining each of these pillars in more detail after you ve worked your way through them your new web programmer eyes will have you looking at your favorite sites very differently conclusion web programming is creating documents with html styling positioning the documents content with css and updating that content and servers based on events using javascript we can break down the javascript part of web programming into three pillars that involve working with the dom javascript eventing and communication with the server now that we ve seen how these pillars are connected in the abstract we are ready to dive into seeing how they work together in detail | front_end |
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laptop | agendrix laptop laptop is a script to set up an macos laptop for web and mobile development it can be run multiple times on the same machine safely it installs upgrades or skips packages based on what is already installed on the machine this project is based on thoughtbot s laptop script available here https github com thoughtbot laptop install download the script sh curl remote name https raw githubusercontent com agendrix laptop master mac review the script avoid running scripts you haven t read sh less mac execute the downloaded script sh sh mac 2 1 tee laptop log optionally review the log sh less laptop log contributing 1 fork the repository on github 2 create a named feature branch i e gitignore addons 3 write you change 4 submit a pull request about img src https user images githubusercontent com 25696312 72089341 dde0a300 32d9 11ea 818d 3f27de8b12e5 png alt agendrix logo height 45 agendrix http www agendrix com is a team of passionate on a mission to create more pleasant and productive workplaces with innovative software an exceptional team and unparalleled customer service license this project is agendrix http www agendrix com it is free software and may be redistributed under the terms specified in the license file | front_end |
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style-guide | style guide javascript style guide javascript md react js style guide react js md eslint eslint plugin import eslint plugin react eslint plugin jsx a11y | front_end |
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MapReader | div align center br p align center h1 mapreader h1 h2 a computer vision pipeline for exploring and analyzing images at scale h2 p div p align center a href https pypi org project mapreader img alt pypi src https img shields io pypi v mapreader a a href https github com living with machines mapreader blob main license img alt license src https img shields io badge license mit yellow svg a a href https github com living with machines mapreader actions workflows mr ci yml badge svg img alt integration tests badge src https github com living with machines mapreader actions workflows mr ci yml badge svg a a href https zenodo org badge latestdoi 430661738 img src https zenodo org badge 430661738 svg alt doi a br p what is mapreader div align center figure img src https raw githubusercontent com living with machines mapreader main figs river banner 8bit png alt annotated map with prediction outputs width 70 figure div mapreader is an end to end computer vision cv pipeline for exploring and analyzing images at scale mapreader was developed in the living with machines https livingwithmachines ac uk project to analyze large collections of historical maps but is a generalizable computer vision pipeline which can be applied to any images in a wide variety of domains overview mapreader is a groundbreaking interdisciplinary tool that emerged from a specific set of geospatial historical research questions it was inspired by methods in biomedical imaging and geographic information science which were adapted for use by historians for example in our journal of victorian culture https doi org 10 1093 jvcult vcab009 and geospatial humanities 2022 sigspatial workshop https arxiv org abs 2111 15592 papers the success of the tool subsequently generated interest from plant phenotype researchers working with large image datasets and so mapreader is an example of cross pollination between the humanities and the sciences made possible by reproducible data science mapreader pipeline div align center figure img src https raw githubusercontent com living with machines mapreader main docs source figures pipeline explained png alt mapreader pipeline width 70 figure div the mapreader pipeline consists of a linear sequence of tasks which together can be used to train a computer vision cv classifier to recognize visual features within maps and identify patches containing these features across entire map collections see our about mapreader https mapreader readthedocs io en latest about html page to learn more documentation the mapreader documentation can be found at https mapreader readthedocs io en latest index html new users should refer to the installation instructions https mapreader readthedocs io en latest install html and input guidance https mapreader readthedocs io en latest input guidance html for help with the initial set up of mapreader all users should refer to our user guide https mapreader readthedocs io en latest user guide user guide html for guidance on how to use mapreader this contains end to end instructions on how to use the mapreader pipeline plus a number of worked examples illustrating use cases such as geospatial images i e maps non geospatial images developers and contributors may also want to refer to the api documentation https mapreader readthedocs io en latest api index html and contribution guide https mapreader readthedocs io en latest contribution guide html for guidance on how to contribute to the mapreader package join our slack workspace please fill out this form https forms gle dxjechzqkwrz3xpt9 to receive an invitation to the slack workspace what is included in this repo the mapreader package provides a set of tools to download images maps and metadata stored on web servers e g tileservers which can be used to retrieve maps from openstreetmap osm the national library of scotland nls or elsewhere load images maps and metadata stored locally pre process images maps patchify create patches from a parent image resample use image transformations to alter pixel dimensions resolution orientation etc remove borders outside the neatline reproject between coordinate reference systems crs annotate images maps or their patches using an interactive annotation tool train or fine tune computer vision cv models and use these to predict labels i e model inference on large sets of images maps various plotting and analysis functionalities are also included based on packages such as matplotlib cartopy google earth and kepler gl https kepler gl how to cite mapreader if you use mapreader in your work please cite both the mapreader repo and our sigspatial paper https dl acm org doi 10 1145 3557919 3565812 kasra hosseini daniel c s wilson kaspar beelen and katherine mcdonough 2022 mapreader a computer vision pipeline for the semantic exploration of maps at scale in proceedings of the 6th acm sigspatial international workshop on geospatial humanities geohumanities 22 association for computing machinery new york ny usa 8 19 https doi org 10 1145 3557919 3565812 kasra hosseini rosie wood andy smith katie mcdonough daniel c s wilson christina last kalle westerling and evangeline mae corcoran living with machines mapreader end of lwm zenodo july 27 2023 https doi org 10 5281 zenodo 8189653 acknowledgements this work was supported by living with machines ahrc grant ah s01179x 1 and the alan turing institute epsrc grant ep n510129 1 living with machines funded by the uk research and innovation ukri strategic priority fund is a multidisciplinary collaboration delivered by the arts and humanities research council ahrc with the alan turing institute the british library and the universities of cambridge east anglia exeter and queen mary university of london maps above reproduced with the permission of the national library of scotland https maps nls uk index html | hut23 hut23-96 computer-vision deep-learning hacktoberfest machine-learning pytorch article digital-humanities maps spatial-data | ai 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natds-js | natura design system lerna https img shields io badge maintained 20with lerna cc00ff svg https lerna js org semantic release https img shields io badge 20 20 f0 9f 93 a6 f0 9f 9a 80 semantic release e10079 svg https github com semantic release semantic release known vulnerabilities https snyk io test github natura cosmeticos natds js badge svg targetfile package json https snyk io test github natura cosmeticos natds js targetfile package json how to install if your project is using react and react dom version 16 8 4 or higher shell script npm npm i save naturacosmeticos natds react yarn yarn add naturacosmeticos natds react the natds react package includes style tokens need more info about this package please refer to natds react documentation packages react readme md troubles please refer to the troubleshooting guide troubleshooting md of this package how to use check our design system storybook https natds web natura design react index html for all react components not a react based project install our style tokens separately please check natds themes docs https github com natura cosmeticos natds commons tree master packages natds themes for more info you can also install icons separately please check natds icons docs https github com natura cosmeticos natds commons tree master packages natds icons for more info how to contribute to contribute please check our contributing guidelines contributing md | design-system react material-design material-ui natura | os |
Building-Computer-Vision-Projects-with-OpenCV4-and-CPlusPlus | github issues https img shields io github issues packtpublishing building computer vision projects with opencv4 and cplusplus svg https github com packtpublishing building computer vision projects with opencv4 and cplusplus issues github forks https img shields io github forks packtpublishing building computer vision projects with opencv4 and cplusplus svg https github com packtpublishing building computer vision projects with opencv4 and cplusplus network github stars https img shields io github stars packtpublishing building computer vision projects with opencv4 and cplusplus svg https github com packtpublishing building computer vision projects with opencv4 and cplusplus stargazers prs welcome https img shields io badge prs welcome brightgreen svg https github com packtpublishing building computer vision projects with opencv4 and cplusplus pulls building computer vision projects with opencv4 and cplusplus implement complex computer vision algorithms and explore deep learning and face detection this course is your guide to understanding opencv concepts and algorithms through real world examples and projects by taking this course you will be able to work on complex projects that involves image processing motion detection and image segmentation what you will learn explore algorithmic design approaches for complex computer vision tasks work with opencv s most updated api through projects understand 3d scene reconstruction and structure from motion study camera calibration and overlay ar using the aruco module create cmake scripts to compile your c application explore segmentation and feature extraction techniques remove backgrounds from static scenes to identify moving objects for surveillance work with new opencv functions to detect and recognize text with tesseract hardware requirements for an optimal student experience we recommend the following hardware configuration processor 2 6 ghz or higher preferably multi core memory 4gb ram hard disk 10gb or more an internet connection macosx machine for example macbook imac running macos high sierra v10 13 software requirements you ll also need the following software installed in advance operating system windows 8 or higher qt opengl tesseract browser latest version of one or more browsers is recommended | jupyter-notebook cmake java c-plus-plus python | ai |
flutter-camscanner | style very good analysis https img shields io badge style very good analysis b22c89 svg https pub dev packages very good analysis uocreactnativeflutter activity 5 exercise 3 this is a capstone project for subject new trends in mobile development course taken at the open universitoy of catalonia www uoc edu what does this project with this project the user can scan documents paper national documents and generate a pdf this pdf can be printed shared or ocr to text libraries in this project http https pub dev packages http used for calling ocr space for passing pdf to text and print in screen edge detection https pub dev packages edge detection the scanner library uses native path provider https pub dev packages path provider used for searching document path across ios android uuid https pub dev packages uuid used for generate a unique identifier for each document scanned pdf https pub dev packages pdf for generating the pdf from images printing https pub dev packages printing rendering sharing and printing the pdf rflutter alert https pub dev packages rflutter alert library with alert 1mb pdf limit of api ocr flutter swiper https pub dev packages flutter swiper carrousel of images in scanning page r dotted line border https pub dev packages r dotted line border decoration of add image in carrousel get it https pub dev packages get it service locator for services injectable https pub dev packages injectable code generator for get it automatically register services into service locator fontisto flutter https pub dev packages fontisto flutter icons build runner https pub dev packages build runner a build system for dart code generation and modular compilation injectable generator https pub dev packages injectable generator code generator for injectable how to regenerate autogenerated code from dependency injection flutter pub run build runner build delete conflicting outputs work in progress in testing images img src images main screen no scan jpg height 300px img src images scan document jpg height 300px img src images document scanning gif height 300px img src images main screen with docs jpg height 300px img src images document viewer jpg height 300px img src images exercise info jpg height 300px | front_end |
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web-development-with-node-and-express-2e | examples web development with node and express 2nd edition work in progress most of the code samples in this repo are complete if difficult to navigate i am in the progress up updating the readme files to make it easier to find your way around and correct any missing documentation please let me know if there s something that you feels needs additional clarification or explanation chapters chapter 1 this is just an introductory chapter with no example code chapter 2 the chapter 2 examples ch02 readme md demonstrate a simple minimal web server using node only no express to provide the reader a little background chapter 3 the chapter 3 examples ch03 readme md demonstrate a simple minimal web server using express chapter 4 the chapter 4 example ch04 readme md take the fortune cookie functionality developed in chapter 3 and implement it as a node module chapter 5 the chapter 5 example ch05 readme md demonstrates unit testing with jest integration testing with jest and puppeteer and linting with eslint chapter 6 the chapter 6 example ch06 readme md uses small examples to demonstrate various useful features of express such as rendering views using cookies and sessions processing forms and providing an api | front_end |
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RedPajama-Data | a href https discord gg 9rk6sseweg img src https img shields io discord 1082503318624022589 label discord a img src https img shields io github license togethercomputer redpajama data redpajama data an open source recipe to reproduce llama training dataset img width 500 src docs redpajama png this repo contains a reproducible data receipe for the redpajama data with the following token counts dataset token count commoncrawl 878 billion c4 175 billion github 59 billion books 26 billion arxiv 28 billion wikipedia 24 billion stackexchange 20 billion total 1 2 trillion data preparation in data prep we provide all pre processing scripts and guidelines tokenization in tokenization we provide an example of how to tokenize the dataset using the gpt neox tokenizer visualization in viz we provide a dashboard for exploring a subset of the data using meerkat https github com hazyresearch meerkat license the code in this repo is licensed under the apache 2 0 license unless otherwise noted copyright 2023 together computer eth z rich stanford university licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license the file data prep book dedup py data prep book dedup py was co developed with ontocord ai https www ontocord ai copyright 2023 ontocord ai together computer eth z rich stanford university licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license the dataset itself please refer to the licenses of the data subsets you use common crawl foundation terms of use https commoncrawl org terms of use full c4 license https huggingface co datasets allenai c4 license github was limited to mit bsd or apache licenses only books the pile books3 license https huggingface co datasets the pile books3 licensing information and pg19 license https huggingface co datasets pg19 licensing information arxiv terms of use https info arxiv org help api tou html wikipedia license https huggingface co datasets wikipedia licensing information stackexchange license on the internet archive https archive org details stackexchange for full terms see the license file if you have any questions comments or concerns about licensing please contact us https www together xyz contact to cite redpajama please use software together2023redpajama author together computer title redpajama an open source recipe to reproduce llama training dataset month april year 2023 url https github com togethercomputer redpajama data acknowledgement we are appreciative to the work done by the growing open source ai community that made this project possible that includes participants in building the redpajama dataset including ontocord ai ontocord ai mila qu bec ai institute https mila quebec en eth ds3lab https ds3lab inf ethz ch universit de montr al https www umontreal ca stanford center for research on foundation models crfm https crfm stanford edu stanford hazy research research group https hazyresearch stanford edu and laion https laion ai eleutherai https www eleuther ai this project is built on the backs of the great team at eleutherai including the source code they provided for training gpt neox an award of computer time was provided by the incite program https www alcf anl gov science incite allocation program this research also used resources of the oak ridge leadership computing facility olcf https www together xyz blog redpajama text resources 20of 20the oak 20ridge 20leadership 20computing 20facility 20 olcf 2c 20which 20is 20a which is a doe office of science user facility supported under contract de ac05 00or22725 | ai |
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simple-wireless-power | simple wireless power system introduction this project is a simple two board wireless power system that can transmit up to 100w at close distances originally it was designed as a small part of a subsea resident autonomous underwater vehicle senior design project the goal was to have a contactless method to charge the vehicles battery in an underwater docking station versus a contact method like a wetmate connector this reduces the chance for failure in a harsh ocean environment included in this repository are schematics pcb design files ltspice simulations and embedded code tx board powered https user images githubusercontent com 32495259 119278762 008b0780 bc02 11eb 916a 1963d50e4152 jpg design topology at the start of my wireless power investigation it was initially decided to create a type of coreless transformer to keep things simple this type of design would transfer power by simply placing two identical coils as close as possible to each other the closer the coils the higher their mutal inductance and coupling coefficient the coupling coefficient directly equals the maximum achievable efficiency k 0 7 would mean 70 efficiency for example coreless transformer https user images githubusercontent com 32495259 119279035 b4d95d80 bc03 11eb 8f76 d7799d2d53da png center coreless transformer equivalent circuit however since a lot of work was already being placed into research and simulation anyways it was decided to try and create a resonant system using capactiors resonant circuits can be created with the inductive transmitter and reciever coils increasing efficiency accross the wireless link this is the de facto inductive wireless power transfer method used in industry and it turned out to be simpler than thought the topology chosen is called series series resonant inductive wireless power transfer which means that the compensating capacitors are placed in series with both the transmit and recieve coils other topologys exist such as series parallel or parallel parallel which place the capacitors in parallel with the coils as well series series https user images githubusercontent com 32495259 119279042 b86ce480 bc03 11eb 8be5 fae0d67579dd png series series topology circuit hardware the system consists of a transmitter tx pcb and a reciever rx pcb both are 4 layer 1oz copper boards that are 100x45mm the designs were completed using kicad eda http kicad pcb org a free and open source pcb design software that is highly recommended tx board the transmitter board s primary purpose is to take a dc input voltage and pass it through a full bridge inverter to create a high frequency sinusoidal voltage this sinusoidal voltage passes through the compensating capacitors and transmitter coil and creates an alternating magnetic field that couples to the reciever coil the h bridge is designed around an operating point of 24vdc input 100khz switching frequency and up to 100w of transfer power the main limiter for power transfer is the rated voltage and current of the coils and compensating capacitors the mosfets of the h bridge are over speced to minimize losses short circuit protection circuitry is included on the tx board since if the tx coil is open loaded during operation it will appear as a short to the input power supply tx board https user images githubusercontent com 32495259 119278773 0bde3300 bc02 11eb 87d0 157e233e9202 png the tx board also has an embedded samd21 microntroller which generates the nessecary pwm signal for the gate drivers further the board includes a can controller and transceiver so that it could potentially be commanded on off and relay power transfer telemetry to a remote control computer via canbus tx schematic https user images githubusercontent com 32495259 119279338 a4c27d80 bc05 11eb 9b8b 24d108d0815e png rx board the rx board is much simpler than the tx board it has no microcontroller and uses passive schottky diodes in a full bridge rectifier configuration to convert the recieved ac voltage back to a usable dc voltage an simple overvoltage protection scheme is incorporated in the rx board to prevent an overvoltage event downstream a schmitt trigger circuit watches the output voltage and if it is too high connects the output to a bank of bleeder resistors the extra load on the output subsequently draws down the voltage before it reaches a harmful level rx board https user images githubusercontent com 32495259 119278781 13054100 bc02 11eb 832a 217c8b096a45 png rx schematic https user images githubusercontent com 32495259 119279342 a9873180 bc05 11eb 946f feee2292f1fa png key components coils the transmitter and reciever coils are identical they were sourced on digi key from wurth electronics pn 732 9670 nd they have an inductance of 5 8uh and a very low series resistance allowing for high current rating i think these are probably the highest current rated coils you can find on digikey when i selected them as well wurth was kind enough to provide ample documentation for the coils a key piece of data was a graph of their quality factor q versus frequency the higher the quality factor the better the efficiency therefore since the q peaked at around 100 101khz that was the selected operating frequency for the entire system coils https user images githubusercontent com 32495259 119278785 17c9f500 bc02 11eb 8290 07031c8aff2d png coils encased and epoxyed in a custom mount for underwater use in the original auv application series capacitors four 100nf capacitors were placed in parallel to achieve the desired 380 400nf series capacitance large 2220 package ceramic capacitors were chosen for their desirable low series resistance the final parts were chosen from murata electronics and sourced on digikey pn 490 16696 1 nd these capacitors were compared against others again looking at their quality factor over frequency the series capacitors ended up being the weakest link in the system and would fail on the tx side when delivering over 5 6a these caps had the best that could be found at the time but if someone knows of a better or higher current carrying choice let me know mosfets the mosfets chosen are low loss n channel devices the final parts chosen are from infineon and sourced on digi key pn 448 iauc70n08s5n074atma1ct nd these fets were selected for the lowest possible loss taking into account both resistive and switching losses diodes the diodes for the full bridge rectifier were chosen again for as low of loss as possible really the low voltage and high current environment is probably better suited for active rectification but to keep things simple passive rectification was chosen the best diodes for this are schottky due to their low forward voltage the final parts chosen are from on semi and sourced from digi key pn fsv12100vct nd a few different diode models were selected and simulated in the final circuit and this model ended up producing the lowest loss micro controller the chosen microcontroller is the samd21g18a pn atsamd21g18a mf nd this is the same microcontroller that is on the arduino zero dev board and our senior design team already knew how to easily program and flash it perhaps someone could modify this design and retrofit a more popular microcontroller like an stm32 software the current software developed for this project is the embedded code on the tx board s samd21 microcontroller code was developed using the arduino ide for simplicity a bin file was generated and then uploaded to the microcontroller using a j link edu programmer the code is about as simple as it gets just generating a 50 duty cycle 100khz pwm signal no feedback is incorporated since it is assumed that there is no expected communication between tx and rx boards in the original underwater application simulation the entire end to end wireless power system was simulated in ltspice the simulation aided in component selection and gave an initial estimate of efficiency greater than 90 it s hard to say how accurate the simulation is since it s not clear what coupling factor k will be present in a real world system any tips on wpt system simulation in ltspice would be greatly appreciated sim schematic https user images githubusercontent com 32495259 119278790 1c8ea900 bc02 11eb 931d c2eacffef1e0 png testing results the system was tested with surpising results at a gap distance of about 1 2mm as close as we could get the system was able to transfer power up to 100w at an efficiency of 81 3 testing different frequencies between 95 105khz did not show any noticeable difference in efficiency the coils were also submerged in saltwater to check efficiency versus the in air test there was no measurable difference in water salt or fresh lab test setup https user images githubusercontent com 32495259 119278795 20bac680 bc02 11eb 9807 420d85a31efa jpg water setup https user images githubusercontent com 32495259 119278801 257f7a80 bc02 11eb 9ca2 66115d665621 png another interesting outcome was shown in the thermal images during testing the hottest components in the system were the series capacitors reaching upwards of 70c degrees after 5 mins of operation no heatsink or airflow meant that these are probably worst case numbers but still perhaps the capacitors could be underspec d for the application at 100w delivered about 20w is lost in the system and the series capacitors are the only noticeable components that heat up significantly it s a bit inconcievable that all the 20w is split amongst 8 small capacitors so i assume that there are some losses in the surrounding air that are radiated away it s hard to tell would definitely be open to some insight on this if anyone has encountered this before capacitor thermals https user images githubusercontent com 32495259 119279590 426a7c80 bc07 11eb 95e8 bb5e12374c14 png efficiency vs coil gap distance measurements were completed with some interesting results first efficiency seems to drop linearly from 0 20mm this is cool since it seems that the resonant effect is indeed working normally a magnetic field would decrease with the distance squared a non linear relationship as well when reaching 20mm or beyond the tx board was limited by its series current capability more series current is required in to the transmitter coil to achieve the same power transfer as efficiency drops the series capacitors on the tx board were the limiter as they started to fail beyond 6a a heatsink and forced air could give them a bit of extra capability potentially i d be interested in hearing about any better capacitor choices for higher series current while maintaining low loss accross them or perhaps if a parallel series or parallel parallel topology would solve this problem gap test https user images githubusercontent com 32495259 121230179 7c738980 c869 11eb 8f39 d84caf056ab7 jpg gapgraph https user images githubusercontent com 32495259 121230193 809fa700 c869 11eb 9149 8e11355a4f93 png discussion this was a fun project and a small part of a much larger senior design project 80 75 efficiency for 0 5mm coil gap satisfies the original design problem which was to safely deliver 50w underwater at a 0 5mm gap distance always open for discussion or any tips on how this could have been done better for a future project i m interested in trying to make a super small low power version that could deliver 5 10w for underwater sensor applications | os |
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web-dev-bootcamp | welcome to the modern web dev boot camp prerequisites create a github account https github com install on your own computer git https git scm com book en v2 getting started installing git aws cli https aws amazon com cli vs code https code visualstudio com download node js https nodejs org en download 18 x or newer slack https slack com downloads tutorial outline table thead tr th time th th topic th th presenter th tr thead tbody tr td colspan 3 b monday 08 14 2023 gsfc building 34 room e215 b td tr tr td 10 00 10 30 td td orientation prerequisites office hours td td td tr tr td 10 30 11 00 td td a href syllabus git lecture md git lecture a td td dakota td tr td 11 00 12 00 td td a href syllabus git exercise md git exercise a td td dakota td tr td 13 00 14 00 td td a href syllabus git exercise md git exercise continued a td td dakota td tr td 14 00 16 30 td td a href syllabus modern javascript md modern javascript lecture a td td courey td tr td 14 00 16 30 td td a href syllabus web dev exercise md web dev exercise a td td courey td tr td colspan 2 b tuesday 08 15 2023 gsfc building 34 room w305 b td td td tr tr td 09 00 11 00 td td a href syllabus tutorial walkthrough md tutorial walkthrough a td td courey td tr td 13 00 13 30 td td a href syllabus cloud basics lecture md cloud basics lecture a td td leo td tr td 13 30 14 00 td td a href syllabus cloud basics exercise md cloud basics exercise a td td leo td tbody table about this repository this repository is based on americana https github com nasa gcn americana stack a project template for remix https remix run preconfigured with the us web design system uswds https designsystem digital gov to start a fresh project from that template without the tutorial materials run this command npx create remix latest template nasa gcn americana stack learn more about remix stacks https remix run stacks | aws javascript jsx nasa remix-run typescript uswds | cloud |
flutter-ui-templates | div align center flutter ui templates welcome contributors the robotics forum always encourages new ideas p open source love svg1 https badges frapsoft com os v1 open source svg v 103 https github com ellerbrock open source badges prs welcome https img shields io badge prs welcome brightgreen svg style flat visitors https api visitorbadge io api visitors path the robotics forum flutter ui templates 20 countcolor 23263759 style flat github forks https img shields io github forks the robotics forum flutter ui templates github repo stars https img shields io github stars the robotics forum flutter ui templates github last commit https img shields io github last commit the robotics forum flutter ui templates github issues https img shields io github issues the robotics forum flutter ui templates github closed issues https img shields io github issues closed raw the robotics forum flutter ui templates github pull requests https img shields io github issues pr the robotics forum flutter ui templates github closed pull requests https img shields io github issues pr closed the robotics forum flutter ui templates p div aim main aim of this repository is to make things faster and convenient for all the flutter developers who just want to save their time in their flutter development this repository consists of all the development widgets that are being commonly used while coding end to end mobile screens in flutter the goal is to save your time by providing the complete code snippet which follows standard dart conventions with a fully responsive and interactive design of all the components that you may use in your development journey at the later stage the project is planned to be converted into a library and packages so that the components created can directly be used by importing and calling through classes objectives creating end to end mobile app templates refinement of pre existing ui work on interactions and effects animations contact us feel free to get in touch with us and join our discussion https github com the robotics forum flutter ui templates discussions for updates project maintainers table align center tr td align center a href https github com pavan49719 img src https avatars githubusercontent com u 90468365 v 4 width 150px height 150px a br h4 style color blue pavan bhadane h4 br a href https www linkedin com in pavan bhadane 033b26205 img src https t0 gstatic com images q tbn and9gcrmca3j2a8hfll9p5uau5nd9lvqllnzvqou4xosz192uh4iys6x width 32px height 32px a a href https github com pavan49719 img src https user images githubusercontent com 85052056 181615792 498908b8 4a88 4854 8a24 07d803e68891 png width 32px height 32px a td td align center a href https github com siddhantpawar03 img src https avatars githubusercontent com u 85052056 v 4 width 150px height 150px a br h4 style color blue siddhant pawar h4 br a href https www linkedin com in siddhant pawar 398a05201 img src https t0 gstatic com images q tbn and9gcrmca3j2a8hfll9p5uau5nd9lvqllnzvqou4xosz192uh4iys6x width 32px height 32px a a href https github com siddhantpawar03 img src https user images githubusercontent com 85052056 181615792 498908b8 4a88 4854 8a24 07d803e68891 png width 32px height 32px a td tr table contributing guidelines click here contributing md organization div align center image https user images githubusercontent com 90468365 178148078 f2740742 9e6d 44e0 aab3 08a74f4aacf1 png https www vitpunerobotics com div open source program this project is a part of following open source programs div align center logo https user images githubusercontent com 85816852 181296374 a2273b6c 45af 45d1 b5d8 ee871a035c1f jpg div | flutter app-templates dart dart-packages flutter-apps flutter-library flutter-packages flutter-templates flutter-ui native-template ui-components ui-design | front_end |
Course-Work-CityU | these are my course work in city university master period br including topics in computer graphics image processing machine learning etc br these work are based on templates offered by lecturer my work was to modify part of them br | server |
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programming-design-systems-projects | about this repository this is an empty repository that students in my programming design systems http printingcode runemadsen com class can use as a starter template for their own projects getting started fork this repo to your own github account open terminal on your computer cd into the folder where you want the main project folder to exist for me this is the projects folder so i run cd projects from my terminal clone your new forked repo into this folder by running something like git clone git github com username programming design systems projects git you need to find the actual git url on your forked project page running the projects you need a web server to see the examples in your browser luckily this repo has a script to do this run the following from inside your project folder bash bash server sh now go to localhost 1234 http localhost 1234 and navigate to any project folder adding your own projects simply make a copy of the first assignment folder and rename it now start coding | os |
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LLM-Next-Item-Rec | zero shot next item recommendation updating code for the paper zero shot next item recommendation using large pretrained language models visitors https visitor badge glitch me badge page id agi edgerunners llm next item rec showcase ps prompting gpt rec main jpg news 04 10 2022 we updated code for zero shot nir on ml 100k br quick start command for zero shot nir on ml 100k python three stage 0 nir py star star history star history chart https api star history com svg repos agi edgerunners llm next item rec type date https star history com agi edgerunners llm next item rec date citations bibtex article wang2023zero title zero shot next item recommendation using large pretrained language models author wang lei and lim ee peng journal arxiv preprint arxiv 2304 03153 year 2023 | ai |
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Genomics-Technologies-and-Analysis-Course | genomics technologies and analysis course information and materials for a bioengineering focused course on genomics technologies and analysis in this set of files you will find the basic materials that i developed to teach a course on genomics focused on bioengineering this course includes information on the basics of genetics and genomic data it explores the technologies used for sequencing dna and for performing basic analysis of these genomes please feel free to use if you have questions i am happy to try to answer them most of the materials can be found on the course overview wiki https github com krthickman genomics technologies and analysis course wiki course overview | server |
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trackerfaltas | trackerfaltas ime s attendance policy is really freaking annoying go 120 points and you re out every unjustified abscence costs you 3 points and every justified abscence costs 1 if that wasn t enough you can t miss more than 25 of any given class in a semester this tracker is to help people keep track of their points and s so they arent desperate by the end of the semester it shows how many points you got what of classes have you missed relative to the total number of classes in the semester and how many abscenses are justified and how many are not the 2 numbers below the the one on the left is justified the one on the right unjustified alt tag http oi57 tinypic com 2cxj37m jpg | os |
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biome-text | p align center br img src https github com recognai biome text raw master docs biome text logo for readme png width 600 br p p align center a href https github com recognai biome text actions img alt ci src https github com recognai biome text workflows ci badge svg branch master event push a a href https codecov io gh recognai biome text img src https codecov io gh recognai biome text branch master graph badge svg token 943463z6f7 a a href https github com recognai biome text blob master license txt img alt github src https img shields io github license recognai biome text svg color blue a a href https www recogn ai biome text img alt documentation src https img shields io website http recognai github io biome text index html svg down color red down message offline up message online a a href https github com recognai biome text releases img alt github release src https img shields io github release recognai biome text svg a p h3 align center p natural language processing library built with allennlp h3 quick links documentation https recognai github io biome text features state of the art and not so state of the art models trained with your own data with simple workflows efficient data reading for large datasets in multiple formats and sources csv parquet json etc modular configuration and extensibility of models datasets and training runs programmatically or via config files use via cli or as plain python e g inside a jupyter notebook compatible with allennlp installation for the installation we recommend setting up a fresh conda https docs conda io en latest miniconda html environment shell script conda create n biome python 3 7 0 pip 20 3 0 conda activate biome once the conda environment is activated you can install the latest release via pip shell script pip install u biome text after installing biome text the best way to test your installation is by running the biome text cli command shell script biome help get started the best way to see how biome text works is to go through our first tutorial https recognai github io biome text master documentation tutorials 1 training a text classifier html please refer to our documentation https recognai github io biome text for more tutorials detailed user guides and how you can contribute https recognai github io biome text master documentation community 1 contributing html to biome text licensing the code in this project is licensed under apache 2 license | nlp natural-language-processing data-science pytorch allennlp | ai |
blockchain-in-js-workshop-2021 | 2021xxxx 2021xxxx 2021xxxx 2021xxxx https github com caosbad blockchain in js workshop 2021 commint https github com cuitblockchain blockchain in js workshop 2021 commit 25f3a0d83a9fff2b4514c5503f470df939d0c2af commint https github com cuitblockchain blockchain in js workshop 2021 commit 25f3a0d83a9fff2b4514c5503f470df939d0c2af commint https github com cuitblockchain blockchain in js workshop 2021 commit 25f3a0d83a9fff2b4514c5503f470df939d0c2af commint https github com cuitblockchain blockchain in js workshop 2021 commit 25f3a0d83a9fff2b4514c5503f470df939d0c2af commint https github com cuitblockchain blockchain in js workshop 2021 commit 25f3a0d83a9fff2b4514c5503f470df939d0c2af commint https github com cuitblockchain blockchain in js workshop 2021 commit 25f3a0d83a9fff2b4514c5503f470df939d0c2af commint https github com cuitblockchain blockchain in js workshop 2021 commit 25f3a0d83a9fff2b4514c5503f470df939d0c2af | blockchain |
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basedata_for_AEEES2021 | basedata for aeees2021 this is the supporting material for paper an optimal dispatching strategy for charging and discharging of electric vehicles based on cloud edge collaboration which is submitted in 2021 the 3rd asia energy and electrical engineering symposium aeees 2021 | cloud |
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awesome-machine-learning-interpretability | awesome machine learning interpretability awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome a maintained and curated list of practical and awesome responsible machine learning resources if you want to contribute to this list and please do read over the contribution guidelines contributing md send a pull request https github com jphall663 awesome machine learning interpretability compare or file an issue https github com jphall663 awesome machine learning interpretability issues new if something you contributed or found here is missing after our september 2023 redeux please check the archive https github com jphall663 awesome machine learning interpretability blob master archive readme md bak contents community and policy community frameworks and guidance https github com jphall663 awesome machine learning interpretability blob master readme md community frameworks and guidance conferences and workshops https github com jphall663 awesome machine learning interpretability blob master readme md conferences and workshops official policy frameworks and guidance https github com jphall663 awesome machine learning interpretability blob master readme md official policy frameworks and guidance evaluation and standards ai incident trackers https github com jphall663 awesome machine learning interpretability blob master readme md ai incident trackers auditing and evaluation resources https github com jphall663 awesome machine learning interpretability blob master readme md auditing and evaluation resources benchmarks https github com jphall663 awesome machine learning interpretability benchmarks common or useful datasets https github com jphall663 awesome machine learning interpretability blob master readme md common or useful datasets general resources comprehensive software examples and tutorials https github com jphall663 awesome machine learning interpretability comprehensive software examples and tutorials free ish books https github com jphall663 awesome machine learning interpretability free ish books glossaries and dictionaries https github com jphall663 awesome machine learning interpretability glossaries and dictionaries open ish classes https github com jphall663 awesome machine learning interpretability open ish classes miscellaneous resources challenges and competitions https github com jphall663 awesome machine learning interpretability challenges and competitions curated bibliographies https github com jphall663 awesome machine learning interpretability blob master readme md curated bibliographies generative ai responsible use guidance https github com jphall663 awesome machine learning interpretability blob master readme md generative ai responsible use guidance list of lists https github com jphall663 awesome machine learning interpretability blob master readme md list of lists technical resources domain specific software https github com jphall663 awesome machine learning interpretability blob master readme md domain specific software machine learning environment management tools https github com jphall663 awesome machine learning interpretability machine learning environment management tools open source access responsible ai software packages https github com jphall663 awesome machine learning interpretability blob master readme md open sourceaccess responsible ai software packages browser https github com jphall663 awesome machine learning interpretability browser c c https github com jphall663 awesome machine learning interpretability cc python https github com jphall663 awesome machine learning interpretability python r https github com jphall663 awesome machine learning interpretability r community and policy community frameworks and guidance this section is for responsible ml guidance put forward by organizations or individuals not for official government guidance 8 principles of responsible ml https ethical institute principles html adversarial ml threat matrix https github com mitre advmlthreatmatrix ai verify https aiverifyfoundation sg what is ai verify ai snake oil https www aisnakeoil com allennlp interpret a framework for explaining predictions of nlp models http sameersingh org files papers allennlp interpret demo emnlp19 pdf anthropic s responsible scaling policy https www files anthropic com production files responsible scaling policy 1 0 pdf biml interactive machine learning risk framework https berryvilleiml com interactive dealing with bias and fairness in ai ml data science systems https docs google com presentation d 17o nzplyua5fcjfugcy1v1 5gfahk7ohaf4dn44nkue debugging machine learning models iclr workshop proceedings https debug ml iclr2019 github io decision points in ai governance https cltc berkeley edu wp content uploads 2020 05 decision points ai governance pdf distill https distill pub fatml principles and best practices https www fatml org resources principles and best practices from principles to practice an interdisciplinary framework to operationalise ai ethics https www ai ethics impact org resource blob 1961130 c6db9894ee73aefa489d6249f5ee2b9f aieig report download hb data pdf google s responsible ai framework https cloud google com responsible ai h2o ai algorithms https github com h2oai h2o tutorials blob master training h2o algos h2o algos cheat sheet 04 25 17 pdf identifying and overcoming common data mining mistakes https support sas com resources papers proceedings proceedings forum2007 073 2007 pdf machine learning quick reference algorithms https support sas com rnd app data mining enterprise miner pdfs machine learning quick ref algorithms mar2017 pdf machine learning quick reference best practices https support sas com rnd app data mining enterprise miner pdfs machine learning quick ref best practices pdf microsoft responsible ai standard v2 https query prod cms rt microsoft com cms api am binary re5cmfl newsguard ai tracking center https www newsguardtech com special reports ai tracking center robust ml https www robust ml org safe and reliable machine learning https www dropbox com s sdu26h96bc0f4l7 fat19 ai reliability final pdf dl 0 sample ai incident response checklist https bnh ai github io resources the foundation model transparency index https crfm stanford edu fmti troubleshooting deep neural networks http josh tobin com assets pdf troubleshooting deep neural networks 01 19 pdf warning signs the future of privacy and security in an age of machine learning https fpf org wp content uploads 2019 09 fpf warningsigns report pdf when not to trust your explanations https docs google com presentation d 10a0pnkwov3a1xchzvy t1mwudtzuizi3scmzvwgsyfm edit you created a machine learning application now make sure it s secure https www oreilly com ideas you created a machine learning application now make sure its secure conferences and workshops this section is for conferences workshops and other major events related to responsible ml aaai conference on artificial intelligence https aaai org conference aaai acm facct fairness accountability and transparency https facctconference org fat ml fairness accountability and transparency in machine learning https www fatml org acm conference on equity and access in algorithms mechanisms and optimization eaamo https eaamo org aies aaai acm conference on ai ethics and society https www aies conference com black in ai https blackinai github io computer vision and pattern recognition cvpr https thecvf com international conference on machine learning icml https icml cc 2021 human ai collaboration in sequential decision making https icml cc virtual 2021 workshop 8367 machine learning for data automated creation privacy bias https icml cc virtual 2021 workshop 8356 icml workshop on algorithmic recourse https icml cc virtual 2021 workshop 8363 icml workshop on human in the loop learning hill https icml cc virtual 2021 workshop 8362 icml workshop on theoretic foundation criticism and application trend of explainable ai https icml cc virtual 2021 workshop 8355 information theoretic methods for rigorous responsible and reliable machine learning itr3 https icml cc virtual 2021 workshop 8365 international workshop on federated learning for user privacy and data confidentiality in conjunction with icml 2021 fl icml 21 https icml cc virtual 2021 workshop 8359 interpretable machine learning in healthcare https icml cc virtual 2021 workshop 8358 self supervised learning for reasoning and perception https icml cc virtual 2021 workshop 8353 the neglected assumptions in causal inference https icml cc virtual 2021 workshop 8349 theory and practice of differential privacy https icml cc virtual 2021 workshop 8376 uncertainty and robustness in deep learning https icml cc virtual 2021 workshop 8374 workshop on computational approaches to mental health icml 2021 https icml cc virtual 2021 workshop 8352 workshop on distribution free uncertainty quantification https icml cc virtual 2021 workshop 8373 workshop on socially responsible machine learning https icml cc virtual 2021 workshop 8347 2022 1st icml 2022 workshop on safe learning for autonomous driving sl4ad https icml cc virtual 2022 workshop 13475 2nd workshop on interpretable machine learning in healthcare imlh https icml cc virtual 2022 workshop 13449 dataperf benchmarking data for data centric ai https icml cc virtual 2022 workshop 13477 disinformation countermeasures and machine learning discoml https icml cc virtual 2022 workshop 13446 responsible decision making in dynamic environments https icml cc virtual 2022 workshop 13453 spurious correlations invariance and stability scis https icml cc virtual 2022 workshop 13461 the 1st workshop on healthcare ai and covid 19 https icml cc virtual 2022 workshop 13469 theory and practice of differential privacy https icml cc virtual 2022 workshop 13448 workshop on human machine collaboration and teaming https icml cc virtual 2022 workshop 13478 2023 2nd icml workshop on new frontiers in adversarial machine learning https icml cc virtual 2023 workshop 21487 2nd workshop on formal verification of machine learning https icml cc virtual 2023 workshop 21471 3rd workshop on interpretable machine learning in healthcare imlh https icml cc virtual 2023 workshop 21486 challenges in deployable generative ai https icml cc virtual 2023 workshop 21481 could it have been different counterfactuals in minds and machines https icml cc virtual 2023 workshop 21482 federated learning and analytics in practice algorithms systems applications and opportunities https icml cc virtual 2023 workshop 21473 generative ai and law genlaw https icml cc virtual 2023 workshop 21490 interactive learning with implicit human feedback https icml cc virtual 2023 workshop 21477 neural conversational ai workshop what s left to teach trustworthy enhanced adaptable capable and human centric chatbots https icml cc virtual 2023 workshop 21485 the second workshop on spurious correlations invariance and stability https icml cc virtual 2023 workshop 21493 knowledge discovery and data mining kdd https kdd org 2023 2nd acm sigkdd workshop on ethical artificial intelligence methods and applications https charliezhaoyinpeng github io eai kdd23 cfp kdd data science for social good 2023 https kdd dssg github io neural information processing systems neurips https neurips cc 2022 5th robot learning workshop trustworthy robotics https neurips cc virtual 2022 workshop 49997 algorithmic fairness through the lens of causality and privacy https neurips cc virtual 2022 workshop 49967 causal machine learning for real world impact https neurips cc virtual 2022 workshop 49993 challenges in deploying and monitoring machine learning systems https neurips cc virtual 2022 workshop 49982 cultures of ai and ai for culture https neurips cc virtual 2022 workshop 49983 empowering communities a participatory approach to ai for mental health https neurips cc virtual 2022 workshop 49984 federated learning recent advances and new challenges https neurips cc virtual 2022 workshop 50002 gaze meets ml https neurips cc virtual 2022 workshop 49990 hcai neurips 2022 human centered ai https neurips cc virtual 2022 workshop 50008 human evaluation of generative models https neurips cc virtual 2022 workshop 49978 human in the loop learning hill workshop at neurips 2022 https neurips cc virtual 2022 workshop 49957 i can t believe it s not better understanding deep learning through empirical falsification https neurips cc virtual 2022 workshop 49960 learning meaningful representations of life https neurips cc virtual 2022 workshop 49966 machine learning for autonomous driving https neurips cc virtual 2022 workshop 49981 progress and challenges in building trustworthy embodied ai https neurips cc virtual 2022 workshop 49972 tackling climate change with machine learning https neurips cc virtual 2022 workshop 49964 trustworthy and socially responsible machine learning https neurips cc virtual 2022 workshop 49959 workshop on machine learning safety https neurips cc virtual 2022 workshop 49986 2023 ai meets moral philosophy and moral psychology an interdisciplinary dialogue about computational ethics https neurips cc virtual 2023 workshop 66528 algorithmic fairness through the lens of time https neurips cc virtual 2023 workshop 66502 attributing model behavior at scale attrib https neurips cc virtual 2023 workshop 66496 backdoors in deep learning the good the bad and the ugly https neurips cc virtual 2023 workshop 66550 computational sustainability promises and pitfalls from theory to deployment https neurips cc virtual 2023 workshop 66523 i can t believe it s not better icbinb failure modes in the age of foundation models https neurips cc virtual 2023 workshop 66506 socially responsible language modelling research solar https neurips cc virtual 2023 workshop 66526 regulatable ml towards bridging the gaps between machine learning research and regulations https neurips cc virtual 2023 workshop 66512 workshop on distribution shifts new frontiers with foundation models https neurips cc virtual 2023 workshop 66509 xai in action past present and future applications https neurips cc virtual 2023 workshop 66529 official policy frameworks and guidance this section serves as a repository for policy documents regulations guidelines and recommendations that govern the ethical and responsible use of artificial intelligence and machine learning technologies from international legal frameworks to specific national laws the resources cover a broad spectrum of topics such as fairness privacy ethics and governance 12 cfr part 1002 equal credit opportunity act regulation b https www consumerfinance gov policy compliance rulemaking regulations 1002 a regulatory framework for ai recommendations for pipeda reform https www priv gc ca en about the opc what we do consultations completed consultations consultation ai reg fw 202011 aiming for truth fairness and equity in your company s use of ai https www ftc gov news events blogs business blog 2021 04 aiming truth fairness equity your companys use ai algorithmic accountability act of 2023 https www govinfo gov app details bills 118hr5628ih algorithm charter for aotearoa new zealand https data govt nz assets data ethics algorithm algorithm charter 2020 final english 1 pdf artificial intelligence ai in the securities industry https www finra org sites default files 2020 06 ai report 061020 pdf assessment list for trustworthy artificial intelligence altai for self assessment shaping europe s digital future european commission https ec europa eu digital single market en news assessment list trustworthy artificial intelligence altai self assessment audit of governance and protection of department of defense artificial intelligence data and technology https media defense gov 2020 jul 01 2002347967 1 1 1 dodig 2020 098 pdf a primer on artificial intelligence in securities markets https www cftc gov media 2846 labcftc primerartificialintelligence102119 download biometric information privacy act https www ilga gov legislation ilcs ilcs3 asp actid 3004 chapterid 57 booker wyden health care letters https www scribd com document 437954989 booker wyden health care letters download california consumer privacy act ccpa https oag ca gov privacy ccpa california privacy rights act cpra https www oag ca gov system files initiatives pdfs 19 0021a1 20 28consumer 20privacy 20 20version 203 29 1 pdf can t lose what you never had claims about digital ownership and creation in the age of generative ai https www ftc gov business guidance blog 2023 08 cant lose what you never had claims about digital ownership creation age generative ai children s online privacy protection rule coppa https www ftc gov legal library browse rules childrens online privacy protection rule coppa civil liability regime for artificial intelligence https www europarl europa eu doceo document ta 9 2020 0276 en pdf consumer data protection act code of virginia https law lis virginia gov vacodefull title59 1 chapter53 data availability and transparency act 2022 australia https www datacommissioner gov au law dat act de identification tools https www nist gov itl applied cybersecurity privacy engineering collaboration space focus areas de id tools department of defense ai principles recommendations on the ethical use of artificial intelligence https media defense gov 2019 oct 31 2002204458 1 1 0 dib ai principles primary document pdf developing financial sector resilience in a digital world selected themes in technology and related risks https www osfi bsif gc ca eng docs tchrsk pdf the digital services act package eu digital services act and digital markets act https digital strategy ec europa eu en policies digital services act package directive on automated decision making canada https www tbs sct gc ca pol doc eng aspx id 32592 eeoc letter from u s senators re hiring software https www bennet senate gov public cache files 0 a 0a439d4b e373 4451 84ed ba333ce6d1dd 672d2e4304d63a04cc3465c3c8bf1d21 letter to chair dhillon pdf facial recognition and biometric technology moratorium act of 2020 https drive google com file d 1gktcjftiemqdsq01dmda49b6hy9zykr8 view fda artificial intelligence machine learning ai ml based software as a medical device samd action plan updated january 2021 https www fda gov media 145022 download fda software as a medical device samd guidance december 8 2017 https www fda gov regulatory information search fda guidance documents software medical device samd clinical evaluation fdic supervisory guidance on model risk management https www fdic gov news financial institution letters 2017 fil17022a pdf federal consumer online privacy rights act copra https www consumerprivacyact com federal fha model risk management model governance guidance https www fhfa gov supervisionregulation advisorybulletins pages model risk management guidance aspx ftc business blog https www ftc gov business guidance blog 2020 04 08 using artificial intelligence and algorithms https www ftc gov business guidance blog 2020 04 using artificial intelligence and algorithms 2021 01 11 facing the facts about facial recognition https www ftc gov business guidance blog 2021 01 facing facts about facial recognition 2021 04 19 aiming for truth fairness and equity in your company s use of ai https www ftc gov business guidance blog 2021 04 aiming truth fairness equity your companys use ai 2022 07 11 location health and other sensitive information ftc committed to fully enforcing the law against illegal use and sharing of highly sensitive data https www ftc gov business guidance blog 2022 07 location health and other sensitive information ftc committed fully enforcing law against illegal 2023 07 25 protecting the privacy of health information a baker s dozen takeaways from ftc cases https www ftc gov business guidance blog 2023 07 protecting privacy health information bakers dozen takeaways ftc cases 2023 08 16 can t lose what you never had claims about digital ownership and creation in the age of generative ai https www ftc gov business guidance blog 2023 08 cant lose what you never had claims about digital ownership creation age generative ai 2023 08 22 for business opportunity sellers ftc says ai stands for allegedly inaccurate https www ftc gov business guidance blog 2023 08 business opportunity sellers ftc says ai stands allegedly inaccurate 2023 09 15 updated ftc hhs publication outlines privacy and security laws and rules that impact consumer health data https www ftc gov business guidance blog 2023 09 updated ftc hhs publication outlines privacy security laws rules impact consumer health data 2023 09 18 companies warned about consequences of loose use of consumers confidential data https www ftc gov business guidance blog 2023 09 companies warned about consequences loose use consumers confidential data 2023 09 27 could privacycon 2024 be the place to present your research on ai privacy or surveillance https www ftc gov business guidance blog 2023 09 could privacycon 2024 be place present your research ai privacy or surveillance 2022 05 20 security beyond prevention the importance of effective breach disclosures https www ftc gov policy advocacy research tech at ftc 2022 05 security beyond prevention importance effective breach disclosures 2023 02 01 security principles addressing underlying causes of risk in complex systems https www ftc gov policy advocacy research tech at ftc 2023 02 security principles addressing underlying causes risk complex systems 2023 06 29 generative ai raises competition concerns https www ftc gov policy advocacy research tech at ftc 2023 06 generative ai raises competition concerns general data protection regulation gdpr https gdpr info eu article 22 eu gdpr automated individual decision making including profiling https www privacy regulation eu en article 22 automated individual decision making including profiling gdpr htm general principles for the use of artificial intelligence in the financial sector https www dnb nl media jkbip2jc general principles for the use of artificial intelligence in the financial sector pdf gouvernance des algorithmes d intelligence artificielle dans le secteur financier france https acpr banque france fr sites default files medias documents 20200612 gouvernance evaluation ia pdf iapp global ai legislation tracker https iapp org resources article global ai legislation tracker iapp us state privacy legislation tracker https iapp org resources article us state privacy legislation tracker innovation spotlight providing adverse action notices when using ai ml models https www consumerfinance gov about us blog innovation spotlight providing adverse action notices when using ai ml models justice in policing act https democrats judiciary house gov issues issue issueid 14924 national conference of state legislatures ncsl 2020 consumer data privacy legislation https www ncsl org technology and communication 2020 consumer data privacy legislation national institute of standards and technology nist ai 100 1 artificial intelligence risk management framework nist ai rmf 1 0 https nvlpubs nist gov nistpubs ai nist ai 100 1 pdf national institute of standards and technology nist four principles of explainable artificial intelligence draft nistir 8312 2020 08 17 https www nist gov system files documents 2020 08 17 nist 20explainable 20ai 20draft 20nistir8312 20 281 29 pdf national institute of standards and technology nist four principles of explainable artificial intelligence nistir 8312 2021 09 29 https www nist gov publications four principles explainable artificial intelligence national science and technology council nstc select committee on artificial intelligence national artificial intelligence research and development strategic plan 2023 update https www whitehouse gov wp content uploads 2023 05 national artificial intelligence research and development strategic plan 2023 update pdf new york city automated decision systems task force report november 2019 https www nyc gov assets adstaskforce downloads pdf ads report 11192019 pdf office of the director of national intelligence odni the aim initiative a strategy for augmenting intelligence using machines https www dni gov files odni documents aim strategy pdf office of management and budget guidance for regulation of artificial intelligence applications finalized november 2020 https www whitehouse gov wp content uploads 2020 11 m 21 06 pdf office of science and technology policy blueprint for an ai bill of rights making automated systems work for the american people https www whitehouse gov ostp ai bill of rights office of the comptroller of the currency occ 2021 model risk management handbook https www occ gov publications and resources publications comptrollers handbook files model risk management index model risk management html online harms white paper full government response to the consultation united kingdom https www gov uk government consultations online harms white paper online privacy act of 2023 https eshoo house gov sites evo subsites eshoo house gov files evo media document 4 12 23 opa pdf online safety bill united kingdom https bills parliament uk bills 3137 principles of artificial intelligence ethics for the intelligence community https www intel gov principles of artificial intelligence ethics for the intelligence community privacy act 1988 australia https www legislation gov au details c2014c00076 proposal for a regulation laying down harmonised rules on artificial intelligence artificial intelligence act https digital strategy ec europa eu en library proposal regulation laying down harmonised rules artificial intelligence artificial intelligence amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence artificial intelligence act and amending certain union legislative acts https www europarl europa eu doceo document ta 9 2023 0236 en html psychological foundations of explainability and interpretability in artificial intelligence https nvlpubs nist gov nistpubs ir 2021 nist ir 8367 pdf the public sector bodies websites and mobile applications accessibility regulations 2018 united kingdom https www legislation gov uk uksi 2018 852 contents made questions and answers to clarify and provide a common interpretation of the uniform guidelines on employee selection procedures https www eeoc gov laws guidance questions and answers clarify and provide common interpretation uniform guidelines questions from the commission on protecting privacy and preventing discrimination https auditor utah gov wp content uploads sites 6 2021 02 office of the state auditor questions to help procuring agencies entities with software procurement feb 1 2021 final pdf re use of external consumer data and information sources in underwriting for life insurance https www dfs ny gov industry guidance circular letters cl2019 01 singapore s compendium of use cases practical illustrations of the model ai governance framework https www pdpc gov sg media files pdpc pdf files resource for organisation ai sgaigovusecases pdf singapore s model artificial intelligence governance framework second edition https www pdpc gov sg media files pdpc pdf files resource for organisation ai sgmodelaigovframework2 pdf supervisory guidance on model risk management https www federalreserve gov supervisionreg srletters sr1107a1 pdf using artificial intelligence and algorithms https www ftc gov news events blogs business blog 2020 04 using artificial intelligence algorithms u s web design system uswds design principles https designsystem digital gov design principles evaluation and standards ai incident trackers this section is for repositories of information related to ai incidents and vulnerabilities these resources should enable ml practitioners to learn from past mistakes not just highlight failures ai incident database responsible ai collaborative https incidentdatabase ai ai vulnerability database avid https avidml org aiaaic https www aiaaic org george washington university law school s ai litigation database https blogs gwu edu law eti ai litigation database verica open incident database void https www thevoid community auditing and evaluation resources we are seeking contributions related to auditing and evaluating ml systems for this section auditboard 5 ai auditing frameworks to encourage accountability https www auditboard com blog ai auditing frameworks auditing machine learning algorithms a white paper for public auditors https www auditingalgorithms net index html censius ai audit https censius ai wiki ai audit crowe llp internal auditor s ai safety checklist https www crowe com insights asset i internal auditors ai safety checklist haptic networks how to perform an ai audit for uk organisations https www haptic networks com cyber security how to perform an ai audit ict institute a checklist for auditing ai systems https ictinstitute nl a checklist for auditing ai systems independent audit of ai systems https forhumanity center independent audit of ai systems institute of internal auditors artificial intelligence auditing framework practical applications part a special edition https www theiia org globalassets documents content articles gpi 2017 december gpi artificial intelligence part ii pdf isaca auditing artificial intelligence https ec europa eu futurium en system files ged auditing artificial intelligence pdf isaca auditing guidelines for artificial intelligence https www isaca org resources news and trends newsletters atisaca 2020 volume 26 auditing guidelines for artificial intelligence forhumanity body of knowledge bok https forhumanity center bok google closing the ai accountability gap defining an end to end framework for internal algorithmic auditing https dl acm org doi abs 10 1145 3351095 3372873 government accountability office artificial intelligence an accountability framework for federal agencies and other entities https www gao gov products gao 21 519sp real world strategies for model debugging https towardsdatascience com strategies for model debugging aa822f1097ce recosense phases of an ai data audit assessing opportunity in the enterprise https recosenselabs com blog phases of an ai data audit assessing opportunity in the enterprise singapore s companion to the model ai governance framework implementation and self assessment guide for organizations https www pdpc gov sg media files pdpc pdf files resource for organisation ai sgisago pdf taskade ai audit pbc request checklist template https www taskade com templates engineering audit pbc request checklist techtarget 9 questions to ask when auditing your ai systems https www techrepublic com article 9 questions to ask when auditing your ai systems testing and debugging in machine learning https developers google com machine learning testing debugging unite ai how to perform an ai audit in 2023 https www unite ai how to perform an ai audit in 2023 benchmarks this section contains benchmarks or datasets used for benchmarks for ml systems particularly those related to responsible ml desiderata benchm ml https github com szilard benchm ml bias benchmark for qa dataset bbq https github com nyu mll bbq helm https crfm stanford edu helm latest nvidia mlperf https www nvidia com en us data center resources mlperf benchmarks openml benchmarking suites https www openml org search type benchmark study type task truthfulqa https github com sylinrl truthfulqa winogender schemas https github com rudinger winogender schemas real toxicity prompts allen institute for ai https allenai org data real toxicity prompts common or useful datasets this section contains datasets that are commonly used in responsible ml evaulations or repositories of interesting important data sources adult income dataset https www kaggle com datasets wenruliu adult income dataset balanced faces in the wild https github com visionjo facerec bias bfw compas recidivism risk score data and analysis https www propublica org datastore dataset compas recidivism risk score data and analysis data repositories all lending club loan data https www kaggle com datasets wordsforthewise lending club amazon open data https registry opendata aws amazon reviews data gov https data gov home mortgage disclosure act hmda data https www consumerfinance gov data research hmda mimic iii clinical database https physionet org content mimiciii 1 4 uci ml data repository https archive ics uci edu fannie mae single family loan performance https capitalmarkets fanniemae com credit risk transfer single family credit risk transfer fannie mae single family loan performance data nypd stop question and frisk data https www nyc gov site nypd stats reports analysis stopfrisk page statlog german credit data https archive ics uci edu dataset 144 statlog german credit data wikipedia talk labels personal attacks https www kaggle com datasets jigsaw team wikipedia talk labels personal attacks general resources comprehensive software examples and tutorials this section is a curated collection of guides and tutorials that simplify responsible ml implementation it spans from basic model interpretability to advanced fairness techniques suitable for both novices and experts the resources cover topics like compas fairness analyses and explainable machine learning via counterfactuals compas analysis using aequitas https github com dssg aequitas blob master docs source examples compas demo ipynb explaining quantitative measures of fairness with shap https github com slundberg shap blob master notebooks overviews explaining 20quantitative 20measures 20of 20fairness ipynb getting a window into your black box model http projects rajivshah com inter reasoncode nfl html from glm to gbm part 1 https www h2o ai blog from glm to gbm part 1 from glm to gbm part 2 https www h2o ai blog from glm to gbm part 2 iml https mybinder org v2 gh christophm iml master filepath notebooks tutorial intro ipynb interpretable machine learning with python https github com jphall663 interpretable machine learning with python interpreting machine learning models with the iml package http uc r github io iml pkg interpretable machine learning using counterfactuals https docs seldon io projects alibi en v0 2 0 examples cf mnist html machine learning explainability by kaggle learn https www kaggle com learn machine learning explainability model interpretability with dalex http uc r github io dalex model interpretation series by dipanjan dj sarkar the importance of human interpretable machine learning https towardsdatascience com human interpretable machine learning part 1 the need and importance of model interpretation 2ed758f5f476 model interpretation strategies https towardsdatascience com explainable artificial intelligence part 2 model interpretation strategies 75d4afa6b739 hands on machine learning model interpretation https towardsdatascience com explainable artificial intelligence part 3 hands on machine learning model interpretation e8ebe5afc608 interpreting deep learning models for computer vision https medium com google developer experts interpreting deep learning models for computer vision f95683e23c1d partial dependence plots in r https journal r project org archive 2017 rj 2017 016 piml toolbox examples https github com selfexplainml piml toolbox tree main examples saliency maps for deep learning https medium com thelastalias saliency maps for deep learning part 1 vanilla gradient 1d0665de3284 visualizing ml models with lime http uc r github io lime visualizing and debugging deep convolutional networks https rohitghosh github io 2018 01 05 visualising debugging deep neural networks what does a cnn see https colab research google com drive 1xm6uz9odpgdnhbljz0rglhv kbrz4e 9 free ish books this section contains books that can be reasonably described as free including some historical books dealing broadly with ethical and responsible tech c sar a hidalgo diana orghian jordi albo canals filipa de almeida and natalia martin 2021 how humans judge machines https archive org details mit press book 9780262363266 charles perrow 1984 normal accidents living with high risk technologies https archive org details normalaccidentsl0000perr charles perrow 1999 normal accidents living with high risk technologies with a new afterword and a postscript on the y2k problem https archive org details normalaccidentsl00perr christoph molnar 2021 interpretable machine learning a guide for making black box models explainable https christophm github io interpretable ml book christophm interpretable ml book https github com christophm interpretable ml book deborah g johnson and keith w miller 2009 computer ethics analyzing information technology fourth edition https archive org details computerethicsan0004edjohn george reynolds 2002 ethics in information technology https archive org details ethicsininformat00reyn george reynolds 2002 ethics in information technology instructor s edition https archive org details ethicsininformat0000reyn kenneth vaux editor 1970 who shall live medicine technology ethics https archive org details whoshalllivemedi0000hous kush r varshney 2022 trustworthy machine learning concepts for developing accurate fair robust explainable transparent inclusive empowering and beneficial machine learning systems http www trustworthymachinelearning com marsha cook woodbury 2003 computer and information ethics https archive org details computerinformat0000wood q3r6 m david ermann mary b williams and claudio gutierrez 1990 computers ethics and society https archive org details computersethicss0000unse morton e winston and ralph d edelbach 2000 society ethics and technology first edition https archive org details societyethicstec00wins morton e winston and ralph d edelbach 2003 society ethics and technology second edition https archive org details societyethicstec0000unse morton e winston and ralph d edelbach 2006 society ethics and technology third edition https archive org details societyethicstec00edel patrick hall and navdeep gill 2019 an introduction to machine learning interpretability an applied perspective on fairness accountability transparency and explainable ai second edition https h2o ai content dam h2o en marketing documents 2019 08 an introduction to machine learning interpretability second edition pdf patrick hall navdeep gill and benjamin cox 2021 responsible machine learning actionable strategies for mitigating risks driving adoption https info h2o ai rs 644 pkx 778 images oreilly responsible ml ebook pdf patrick hall james curtis parul pandey and agus sudjianto 2023 machine learning for high risk applications approaches to responsible ai https pages dataiku com oreilly responsible ai paula boddington 2017 towards a code of ethics for artificial intelligence https archive org details towardscodeofeth0000bodd przemyslaw biecek and tomasz burzykowski 2020 explanatory model analysis explore explain and examine predictive models with examples in r and python https ema drwhy ai raymond e spier 2003 science and technology ethics https archive org details sciencetechnolog0000unse k7m6 richard a spinello 1995 ethical aspects of information technology https archive org details ethicalaspectsof00spin richard a spinello 1997 case studies in information and computer ethics https archive org details unset0000unse l0l0 richard a spinello 2003 case studies in information technology ethics second edition https archive org details casestudiesininf02edspin solon barocas moritz hardt and arvind narayanan 2022 fairness and machine learning limitations and opportunities https fairmlbook org soraj hongladarom and charles ess 2007 information technology ethics cultural perspectives https archive org details informationtechn0000unse k8c9 stephen h unger 1982 controlling technology ethics and the responsible engineer first edition https archive org details controllingtechn0000unge y4t3 stephen h unger 1994 controlling technology ethics and the responsible engineer second edition https archive org details controllingtechn0000unge glossaries and dictionaries this section features a collection of glossaries and dictionaries that are geared toward defining terms in ml including some historical dictionaries a i for anyone the a z of ai https www aiforanyone org glossary alan turing institute data science and ai glossary https www turing ac uk news data science and ai glossary appen artificial intelligence glossary https appen com ai glossary brookings the brookings glossary of ai and emerging technologies https www brookings edu articles the brookings glossary of ai and emerging technologies center for security and emerging technology glossary https cset georgetown edu glossary comptia artificial intelligence ai terminology a glossary for beginners https connect comptia org content articles artificial intelligence terminology council of europe artificial intelligence glossary https www coe int en web artificial intelligence glossary coursera artificial intelligence ai terms a to z glossary https www coursera org articles ai terms dataconomy ai dictionary be a native speaker of artificial intelligence https dataconomy com 2022 04 23 artificial intelligence terms ai glossary dennis mercadal 1990 dictionary of artificial intelligence https archive org details dictionaryofarti0000merc g2 70 a to z artificial intelligence terms in technology https www g2 com articles artificial intelligence terms general services administration ai guide for government key ai terminology https coe gsa gov coe ai guide for government what is ai key terminology google developers machine learning glossary https developers google com machine learning glossary h2o ai glossary https docs h2o ai h2o latest stable h2o docs glossary html iapp glossary of privacy terms https iapp org resources glossary iapp international definitions of artificial intelligence https iapp org media pdf resource center international definitions of ai pdf iapp key terms for ai governance https iapp org media pdf resource center key terms for ai governance pdf ibm ai glossary https www ibm com cloud architecture architecture practices cognitive glossary iso information technology artificial intelligence artificial intelligence concepts and terminology https standards iso org ittf publiclyavailablestandards iso iec 22989 2022 ed 1 id 74296 publication pdf en zip jerry m rosenberg 1986 dictionary of artificial intelligence robotics https archive org details dictionaryofarti00rose makeuseof a glossary of ai jargon 29 ai terms you should know https www makeuseof com glossary ai jargon terms moveworks ai terms glossary https www moveworks com us en resources ai terms glossary nist airc the language of trustworthy ai an in depth glossary of terms https airc nist gov ai rmf knowledge base glossary oliver houd 2004 dictionary of cognitive science neuroscience psychology artificial intelligence linguistics and philosophy https archive org details dictionaryofcogn0000unse otto vollnhals 1992 a multilingual dictionary of artificial intelligence english german french spanish italian https archive org details multilingualdict0000voll raoul smith 1989 the facts on file dictionary of artificial intelligence https archive org details factsonfiledicti00smit raoul smith 1990 collins dictionary of artificial intelligence https archive org details collinsdictionar0000unse w3w7 salesforce ai from a to z the generative ai glossary for business leaders https www salesforce com blog generative ai glossary stanford university hai artificial intelligence definitions https hai stanford edu sites default files 2023 03 ai key terms glossary definition pdf techtarget artificial intelligence glossary 60 terms to know https www techtarget com whatis feature artificial intelligence glossary 60 terms to know telus international 50 ai terms every beginner should know https www telusinternational com insights ai data article 50 beginner ai terms you should know vair vocabulary of ai risks https delaramglp github io vair wikipedia glossary of artificial intelligence https en wikipedia org wiki glossary of artificial intelligence william j raynor jr 1999 the international dictionary of artificial intelligence first edition https archive org details internationaldic0000rayn mode 2up william j raynor jr 2009 international dictionary of artificial intelligence second edition https archive org details internationaldic0000rayn t1n5 mode 2up open ish classes this section features a selection of educational courses focused on ethical considerations and best practices in ml the classes range from introductory courses on data ethics to specialized training in fairness and trustworthy deep learning an introduction to data ethics https www scu edu ethics focus areas technology ethics resources an introduction to data ethics certified ethical emerging technologist https certnexus com certification ceet cs103f ethical foundations of computer science https www cs utexas edu ans classes cs109 schedule html fairness in machine learning https fairmlclass github io fast ai data ethics course http ethics fast ai syllabus human centered machine learning http courses mpi sws org hcml ws18 introduction to ai ethics https www kaggle com code var0101 introduction to ai ethics info 4270 ethics and policy in data science https docs google com document d 1gv97qqvjqnvym2i01vuraawhe9pqaz9pbp7kkkveg1o introduction to responsible machine learning https jphall663 github io gwu rml machine learning fairness by google https developers google com machine learning crash course fairness video lecture trustworthy deep learning https berkeley deep learning github io cs294 131 s19 miscellaneous resources challenges and competitions this section contains challenges and competitions related to responsible ml fico explainable machine learning challenge https community fico com s explainable machine learning challenge national fair housing alliance hackathon https nationalfairhousing org hackathon2023 twitter algorithmic bias https hackerone com twitter algorithmic bias type team curated bibliographies we are seeking curated bibliographies related to responsible ml across various topics see issue 115 https github com jphall663 awesome machine learning interpretability issues 115 bibtex proposed guidelines for responsible use of explainable machine learning presentation bibliography https github com jphall663 kdd 2019 blob master bibliography bib proposed guidelines for responsible use of explainable machine learning paper bibliography https github com jphall663 responsible xai blob master responsible xai bib a responsible machine learning workflow paper bibliography https github com h2oai article information 2019 blob master back up article information 2019 bib bak web fairness accountability and transparency in machine learning fat ml scholarship https www fatml org resources relevant scholarship generative ai responsible use guidance and tools this section contains information related to the responsible use of generative ai andreessen horowitz a16z ai canon https a16z com ai canon dair prompt engineering guide https www promptingguide ai dair prompt engineering guide github https github com dair ai prompt engineering guide beyond memorization violating privacy via inference with large language models https llm privacy org ethical and social risks of harm from language models https www deepmind com publications ethical and social risks of harm from language models evaluating llms is a minefield https www cs princeton edu arvindn talks evaluating llms minefield google privacy terms generative ai prohibited use policy https policies google com terms generative ai use policy johnsnowlabs langtest https github com johnsnowlabs langtest large language models explained with a minimum of math and jargon https www understandingai org p large language models explained with llama 2 responsible use guide https ai meta com llama responsible use guide open sourcing highly capable foundation models https www governance ai research paper open sourcing highly capable foundation models pai s responsible practices for synthetic media a framework for collective action partnership on ai https syntheticmedia partnershiponai org the rise of generative ai and the coming era of social media manipulation 3 0 next generation chinese astroturfing and coping with ubiquitous ai https www rand org pubs perspectives pea2679 1 html list of lists this section links to other lists of responsible ml or related resources a living and curated collection of explainable ai methods https utwente dmb github io xai papers ai ethics guidelines global inventory https algorithmwatch org en project ai ethics guidelines global inventory ai ethics resources https www fast ai posts 2018 09 24 ai ethics resources html ai tools and platforms https docs google com spreadsheets u 2 d 10ppqymynnyb6zshokxbjj704e0xuj2vj9hcdfozxaoa htmlview awesome interpretable machine learning https github com lopusz awesome interpretable machine learning awesome ml model governance https github com visenger awesome ml model governance awesome mlops https github com visenger awesome mlops awesome production machine learning https github com ethicalml awesome machine learning operations awful ai https github com daviddao awful ai criticalml https github com rockita criticalml machine learning ethics references https github com radames machine learning ethics references machine learning interpretability resources https github com h2oai mli resources oecd nist catalogue of ai tools and metrics https oecd ai en catalogue overview private ai resources https github com openmined private ai resources responsibleai https romanlutz github io responsibleai worldwide ai ethics a review of 200 guidelines and recommendations for ai governance https doi org 10 1016 j patter 2023 100857 xai resources https github com pbiecek xai resources xaience https github com andreysharapov xaience technical resources domain specific software this section curates specialized software tools aimed at responsible ml within specific domains such as in healthcare finance or social sciences machine learning environment management tools this section contains open source or open access ml environment management software dvc https dvc org gigantum https github com gigantum mlflow https mlflow org mlmd https github com google ml metadata modeldb https github com vertaai modeldb neptune https neptune ai researchers open source access responsible ai software packages this section contains open source or open access software used to implement responsible ml browser discrilens https github com wangqianwen0418 discrilens manifold https github com uber manifold tensorboard projector http projector tensorflow org what if tool https pair code github io what if tool index html about c c born again tree ensembles https github com vidalt ba trees certifiably optimal rule lists https github com nlarusstone corels python acd https github com csinva hierarchical dnn interpretations aequitas https github com dssg aequitas ai fairness 360 https github com trusted ai aif360 ai explainability 360 https github com ibm aix360 alepython https github com blent ai alepython aletheia https github com selfexplainml aletheia allennlp https github com allenai allennlp algofairness https github com algofairness alibi https github com seldonio alibi anchor https github com marcotcr anchor bayesian case model https users cs duke edu cynthia code bcm zip bayesian ors of ands https github com wangtongada boa bayesian rule list brl https users cs duke edu cynthia code brl supplement code zip blackboxauditing https github com algofairness blackboxauditing casme https github com kondiz casme causal discovery toolbox https github com fentechsolutions causaldiscoverytoolbox captum https github com pytorch captum causalml https github com uber causalml cdt15 https github com cdt15 checklist https github com marcotcr checklist cleverhans https github com cleverhans lab cleverhans contextual ai https github com sap contextual ai contrastiveexplanation foil trees https github com marcelrobeer contrastiveexplanation counterfit https github com azure counterfit dalex https github com modeloriented dalex debiaswe https github com tolga b debiaswe deepexplain https github com marcoancona deepexplain deeplift https github com kundajelab deeplift deepvis https github com yosinski deep visualization toolbox dianna https github com dianna ai dianna dice https github com interpretml dice dowhy https github com microsoft dowhy ecco https github com jalammar ecco eli5 https github com teamhg memex eli5 explabox https github com marcelrobeer explabox explainable boosting machine ebm ga2m https github com interpretml interpret explainaboard https github com neulab explainaboard explainerdashboard https github com oegedijk explainerdashboard fair classification https github com mbilalzafar fair classification fairml https github com adebayoj fairml fairlearn https github com fairlearn fairlearn fairness comparison https github com algofairness fairness comparison fairness measures code https github com megantosh fairness measures code falling rule list frl https users cs duke edu cynthia code falling rule list zip foolbox https github com bethgelab foolbox grad cam https github com topics grad cam github topic gplearn https github com trevorstephens gplearn h2o 3 penalized generalized linear models http docs h2o ai h2o latest stable h2o py docs modeling html h2ogeneralizedlinearestimator monotonic gbm http docs h2o ai h2o latest stable h2o py docs modeling html h2ogradientboostingestimator sparse principal components glrm http docs h2o ai h2o latest stable h2o py docs modeling html h2ogeneralizedlowrankestimator hate functional tests https github com paul rottger hate functional tests imodels https github com csinva imodels innvestigate neural nets https github com albermax innvestigate integrated gradients https github com ankurtaly integrated gradients interpret https github com interpretml interpret interpret with rules https github com clips interpret with rules keras vis https github com raghakot keras vis keract https github com philipperemy keract l2x https github com jianbo lab l2x learning fair representations https github com zjelveh learning fair representations lime https github com marcotcr lime lift https github com linkedin lift lit https github com pair code lit lofo importance https github com aerdem4 lofo importance lrp toolbox https github com sebastian lapuschkin lrp toolbox mindsdb https github com mindsdb mindsdb mlextend http rasbt github io mlxtend ml fairness gym https github com google ml fairness gym ml privacy meter https github com privacytrustlab ml privacy meter monotonic http xgboost readthedocs io en latest tutorials monotonic html xgboost http xgboost readthedocs io en latest multilayer logical perceptron mllp https github com 12wang3 mllp optbinning https github com guillermo navas palencia optbinning optimal sparse decision trees https github com xiyanghu osdt parity fairness https pypi org project parity fairness pdpbox https github com saucecat pdpbox piml toolbox https github com selfexplainml piml toolbox protopnet https github com cfchen duke pybreakdown https github com mi2datalab pybreakdown pycebox https github com austinrochford pycebox pygam https github com dswah pygam pymc3 https github com pymc devs pymc3 pyss3 https github com sergioburdisso pyss3 pytorch innvestigate https github com fgxaos pytorch innvestigate rationale https github com taolei87 rcnn tree master code rationale responsibly https github com responsiblyai responsibly revise tool https github com princetonvisualai revise tool robustness https github com madrylab robustness rise https github com eclique rise risk slim https github com ustunb risk slim sage https github com iancovert sage salib https github com salib salib scikit learn decision trees http scikit learn org stable modules tree html generalized linear models http scikit learn org stable modules linear model html sparse principal components http scikit learn org stable modules decomposition html sparse principal components analysis sparsepca and minibatchsparsepca scikit fairness https github com koaning scikit fairness scikit multiflow https scikit multiflow github io shap https github com slundberg shap shapley https github com benedekrozemberczki shapley sklearn expertsys https github com tmadl sklearn expertsys skope rules https github com scikit learn contrib skope rules solas ai disparity https github com solasai solas ai disparity super sparse linear integer models slims https github com ustunb slim python tensorflow lattice https github com tensorflow lattice tensorflow lucid https github com tensorflow lucid tensorflow fairness indicators https github com tensorflow fairness indicators tensorflow model analysis https github com tensorflow model analysis tensorflow model card toolkit https github com tensorflow model card toolkit tensorflow model remediation https github com tensorflow model remediation tensorflow privacy https github com tensorflow privacy tensorflow tcav https github com tensorflow tcav tensorfuzz https github com brain research tensorfuzz tensorwatch https github com microsoft tensorwatch textfooler https github com jind11 textfooler text explainability https text explainability readthedocs io text sensitivity https text sensitivity readthedocs io tf explain https github com sicara tf explain themis https github com laser umass themis themis ml https github com cosmicbboy themis ml treeinterpreter https github com andosa treeinterpreter woe https github com boredbird woe xai https github com ethicalml xai xdeep https github com datamllab xdeep xplique https github com deel ai xplique yellowbrick https github com districtdatalabs yellowbrick r aleplot https cran r project org web packages aleplot index html arules https cran r project org web packages arules index html causal svm https github com shangtai githubcausalsvm dalex https github com modeloriented dalex dalextra https cran r project org web packages dalextra index html drwhyai https github com modeloriented drwhy elasticnet https cran r project org web packages elasticnet index html explainprediction https github com rmarko explainprediction explainable boosting machine ebm ga2m https cran r project org web packages interpret index html fairmodels https github com modeloriented fairmodels fairness https cran r project org web packages fairness index html fastshap https github com bgreenwell fastshap featureimportance https github com giuseppec featureimportance flashlight https github com mayer79 flashlight forestmodel https cran r project org web packages forestmodel index html fscaret https cran r project org web packages fscaret gam https cran r project org web packages gam index html glm2 https cran r project org web packages glm2 glmnet https cran r project org web packages glmnet index html h2o 3 penalized generalized linear models http docs h2o ai h2o latest stable h2o r docs reference h2o glm html monotonic gbm http docs h2o ai h2o latest stable h2o r docs reference h2o gbm html sparse principal components glrm http docs h2o ai h2o latest stable h2o r docs reference h2o glrm html ibreakdown https github com modeloriented ibreakdown icebox https cran r project org web packages icebox index html iml https github com christophm iml ingredients https github com modeloriented ingredients intepret https cran r project org web packages interpret index html lightgbmexplainer https github com lantanacamara lightgbmexplainer lime https github com thomasp85 lime live https cran r project org web packages live index html mcr https github com aaronjfisher mcr modeldown https cran r project org web packages modeldown index html modeloriented https github com modeloriented modelstudio https github com modeloriented modelstudio monotonic http xgboost readthedocs io en latest tutorials monotonic html xgboost http xgboost readthedocs io en latest quantreg https cran r project org web packages quantreg index html rpart https cran r project org web packages rpart index html rulefit http statweb stanford edu jhf r rulefit html scalable bayesian rule lists sbrl https users cs duke edu cynthia code sbrl 1 0 tar gz shapflex https github com nredell shapflex shapleyr https github com redichh shapleyr shapper https cran r project org web packages shapper index html smbinning https cran r project org web packages smbinning index html vip https github com koalaverse vip xgboostexplainer https github com applieddatasciencepartners xgboostexplainer | fairness xai interpretability iml fatml accountability transparency machine-learning data-science data-mining python r awesome awesome-list machine-learning-interpretability interpretable-machine-learning interpretable-ml interpretable-ai interpretable-deep-learning explainable-ml | ai |
nebula-insights | asciiarmor google cloud scheduler github api server google cloud functions docker hub api server google bigquery aliyun oss api google data studio toc api explorations github api ref https pygithub readthedocs io en latest examples mainclass html get repo python g github login or token token timeout 60 retry retry total 10 status forcelist 500 502 504 backoff factor 0 3 org str vesoft inc org g get organization org str repos org get repos list repos repository full name vesoft inc nebula repository full name vesoft inc nebula docs repository full name vesoft inc nebula dev docker repository full name vesoft inc github statistics repository full name vesoft inc nebula docker compose repository full name vesoft inc nebula go repository full name vesoft inc nebula java repository full name vesoft inc nebula python repository full name vesoft inc nebula importer repository full name vesoft inc nebula third party repository full name vesoft inc nebula storage repository full name vesoft inc nebula graph repository full name vesoft inc nebula common repository full name vesoft inc nebula stats exporter repository full name vesoft inc nebula web docker repository full name vesoft inc nebula bench repository full name vesoft inc nebula console repository full name vesoft inc nebula docs cn repository full name vesoft inc nebula chaos repository full name vesoft inc nebula clients repository full name vesoft inc nebula spark utils repository full name vesoft inc nebula node repository full name vesoft inc nebula rust repository full name vesoft inc nebula cpp repository full name vesoft inc nebula http gateway repository full name vesoft inc nebula flink connector repository full name vesoft inc nebula community repository full name vesoft inc nebula br repository full name vesoft inc github get clones traffic note clone traffic api doesn t provide historical accumulation for counts but only 15 days stats instead python in 16 repo0 get clones traffic out 16 count 362 uniques 150 clones clones uniques 5 timestamp 2021 04 06 00 00 00 count 16 clones uniques 8 timestamp 2021 04 07 00 00 00 count 23 clones uniques 13 timestamp 2021 04 08 00 00 00 count 30 clones uniques 33 timestamp 2021 04 09 00 00 00 count 45 clones uniques 2 timestamp 2021 04 10 00 00 00 count 13 clones uniques 6 timestamp 2021 04 11 00 00 00 count 19 clones uniques 15 timestamp 2021 04 12 00 00 00 count 28 clones uniques 40 timestamp 2021 04 13 00 00 00 count 54 clones uniques 9 timestamp 2021 04 14 00 00 00 count 21 clones uniques 10 timestamp 2021 04 15 00 00 00 count 34 clones uniques 10 timestamp 2021 04 16 00 00 00 count 23 clones uniques 5 timestamp 2021 04 17 00 00 00 count 17 clones uniques 2 timestamp 2021 04 18 00 00 00 count 13 clones uniques 9 timestamp 2021 04 19 00 00 00 count 23 clones uniques 3 timestamp 2021 04 20 00 00 00 count 3 get view traffic note view traffic api doesn t provide historical accumulation for counts but only 15 days stats instead python in 17 repo0 get views traffic out 17 count 6019 uniques 1134 views view uniques 52 timestamp 2021 04 06 00 00 00 count 169 view uniques 143 timestamp 2021 04 07 00 00 00 count 569 view uniques 152 timestamp 2021 04 08 00 00 00 count 635 view uniques 134 timestamp 2021 04 09 00 00 00 count 648 view uniques 81 timestamp 2021 04 10 00 00 00 count 318 view uniques 42 timestamp 2021 04 11 00 00 00 count 197 view uniques 127 timestamp 2021 04 12 00 00 00 count 515 view uniques 149 timestamp 2021 04 13 00 00 00 count 580 view uniques 134 timestamp 2021 04 14 00 00 00 count 762 view uniques 141 timestamp 2021 04 15 00 00 00 count 385 view uniques 113 timestamp 2021 04 16 00 00 00 count 284 view uniques 48 timestamp 2021 04 17 00 00 00 count 168 view uniques 35 timestamp 2021 04 18 00 00 00 count 135 view uniques 124 timestamp 2021 04 19 00 00 00 count 492 view uniques 59 timestamp 2021 04 20 00 00 00 count 162 get releases releases python in 31 for rel in repo0 get releases print rel gitrelease title nebula graph v1 2 1 gitrelease title nebula graph v1 2 0 gitrelease title nebula graph v1 1 0 gitrelease title nebula graph v1 0 1 gitrelease title v1 0 0 ga gitrelease title nebula graph v1 0 0 rc4 gitrelease title nebula graph v1 0 0 rc3 gitrelease title nebula graph release v1 0 0 rc2 gitrelease title nebula graph v1 0 0 rc1 gitrelease title nebula graph v1 0 0 beta gitrelease title nebula graph v0 1 0 tag and its assets python in 33 release 121 rels 0 in 34 release 121 tag name out 34 v1 2 1 in 35 release 121 get assets out 35 github paginatedlist paginatedlist at 0x1031edb80 in 36 assets list release 121 get assets in 37 assets out 37 gitreleaseasset url https api github com repos vesoft inc nebula releases assets 35056357 gitreleaseasset url https api github com repos vesoft inc nebula releases assets 35056361 gitreleaseasset url https api github com repos vesoft inc nebula releases assets 35056456 asset download count python in 40 assets 0 browser download url out 40 https github com vesoft inc nebula releases download v1 2 1 nebula 1 2 1 el6 5 x86 64 rpm in 41 assets 0 download count out 41 55 in 43 assets 0 name out 43 nebula 1 2 1 el6 5 x86 64 rpm dockerhub api get repos python in 47 dh client dockerhubclient in 48 dh r dh client get repos vesoft in 54 dh r code out 54 200 in 55 dh r content keys out 55 dict keys count next previous results in 58 dh r content results 0 out 58 user vesoft name nebula graphd namespace vesoft repository type image status 1 description nebula graph graphd service image https github com vesoft inc nebula is private false is automated false can edit false star count 2 pull count 223494 last updated 2021 04 19t19 04 20 324010z is migrated false collaborator count 0 affiliation none hub user vesoft aliyun oss api tbd data etl bigquery and gcs design schema ref https cloud google com bigquery docs nested repeated bigquery schema nebula insights bigquery schema png create table from bigquery table github clone records bq table github clone records nebula insights bq table github clone records png table github release records bq table github releases records nebula insights bq table github releases records png table dockerhub image records bq table dockerhub image records nebula insights bq table dockerhub image records png load json files to bigquery assumptions files should be stored in gcs then to be loaded to bigquery as below gsc will be used for 1 as conf file bucket 2 as archive bucket in json files 3 as bigquery data loading json files source asciiarmor github api server google cloud functions docker hub api server google cloud storage aliyun oss api google bigquery gcs file structure bucket name nebula insights https console cloud google com storage browser nebula insights conf folder conf https console cloud google com storage browser nebula insights conf project nebula insights data archive folder records https console cloud google com storage browser nebula insights records project nebula insights how data is planned to be placed bash records 2021 04 21 github clone stats json records 2021 04 21 github release stats json records 2021 04 21 dockerhub image stats json json file structure ref https cloud google com bigquery docs loading data cloud storage json loading nested and repeated json data this is an example of file in records 2021 04 21 github release stats json it should be put in seperated lines like this json repo foo date 2021 01 22 tag v2 0 1 count 3 assets name a deb url http a com a deb count 1 name a rpm url http a com a rpm count 2 newline repo bar date 2021 01 22 tag v2 0 1 count 7 assets name c deb url http a com c deb count 3 name d rpm url http a com d rpm count 4 and in a pretty view it s like this json repo foo date 2021 01 22 tag v2 0 1 count 3 assets name a deb url http a com a deb count 1 name a rpm url http a com a rpm count 2 newline repo bar date 2021 01 22 tag v2 0 1 count 7 assets name c deb url http a com c deb count 3 name d rpm url http a com d rpm count 4 data conversion into json in chapter reference data format example was put there they should be converted into above json file lines pipline hands on records gcp sdk install gcp sdk cli referring to https cloud google com sdk docs install scheduler job based on gae creation bash gcloud app create create jobs in cloudscheduler console https console cloud google com cloudscheduler pub sub creation bash gcloud pubsub topics create nebula insights cron topic gcloud pubsub subscriptions create cron sub topic nebula insights cron topic functions creation corralate function to pub sub trigerring create functions nebula insights create functions png put code inside a google cloud function create functions code nebula insights create functions code png create bigquery table bash bq mk d data location asia east2 nebula insights bq ls datasetid nebulainsights references dockerhub data dict example json vesoft nebula graphd 223560 vesoft nebula metad 215195 vesoft nebula storaged 193011 vesoft nebula dev 77906 vesoft nebula console 5549 vesoft nebula importer 5352 vesoft nebula graph studio 5220 vesoft nebula http gateway 2849 vesoft nebula http client 2168 vesoft nebula stats exporter 548 vesoft nebula data init 389 vesoft nebula tools 118 vesoft nebula graph http 110 vesoft third party build 68 vesoft nebula dashboard 45 vesoft nebula web console 12 vesoft provider 6 github data dict example json vesoft inc nebula clones 2021 04 07 18 2021 04 08 30 2021 04 09 45 2021 04 10 13 2021 04 11 19 2021 04 12 28 2021 04 13 54 2021 04 14 21 2021 04 15 34 2021 04 16 23 2021 04 17 17 2021 04 18 13 2021 04 19 23 2021 04 20 28 releases v1 2 1 nebula 1 2 1 el6 5 x86 64 rpm 0 nebula 1 2 1 el7 5 x86 64 rpm 14 nebula 1 2 1 el8 x86 64 rpm 0 nebula 1 2 1 ubuntu1604 x86 64 deb 0 nebula 1 2 1 ubuntu1804 x86 64 deb 5 nebula 1 2 1 ubuntu2004 x86 64 deb 1 v1 2 0 nebula 1 2 0 el6 5 x86 64 rpm 32 nebula 1 2 0 el7 5 x86 64 rpm 201 nebula 1 2 0 el8 x86 64 rpm 41 nebula 1 2 0 ubuntu1604 amd64 deb 21 nebula 1 2 0 ubuntu1804 amd64 deb 40 nebula 1 2 0 ubuntu2004 amd64 deb 36 v1 1 0 nebula 1 1 0 el6 5 x86 64 rpm 72 nebula 1 1 0 el7 5 x86 64 rpm 125 nebula 1 1 0 ubuntu1604 amd64 deb 23 nebula 1 1 0 ubuntu1804 amd64 deb 28 v1 0 1 nebula 1 0 1 el6 5 x86 64 rpm 28 nebula 1 0 1 el7 5 x86 64 rpm 127 nebula 1 0 1 ubuntu1604 amd64 deb 18 nebula 1 0 1 ubuntu1804 amd64 deb 39 references cloud scheduler trigger cloud functions https cloud google com scheduler docs tut pub sub | cloud |
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yellowbrick | yellowbrick build status https github com districtdatalabs yellowbrick actions workflows ci yml badge svg branch develop https github com districtdatalabs yellowbrick actions workflows ci yml coverage status https codecov io gh districtdatalabs yellowbrick branch develop graph badge svg token bnaseczz2r https codecov io gh districtdatalabs yellowbrick total alerts https img shields io lgtm alerts g districtdatalabs yellowbrick svg logo lgtm logowidth 18 https lgtm com projects g districtdatalabs yellowbrick alerts language grade python https img shields io lgtm grade python g districtdatalabs yellowbrick svg logo lgtm logowidth 18 https lgtm com projects g districtdatalabs yellowbrick context python pypi version https badge fury io py yellowbrick svg https badge fury io py yellowbrick documentation status https readthedocs org projects yellowbrick badge version latest http yellowbrick readthedocs io en latest badge latest black https img shields io badge code 20style black 000000 svg https github com psf black doi https zenodo org badge doi 10 5281 zenodo 1206239 svg https doi org 10 5281 zenodo 1206239 joss http joss theoj org papers 10 21105 joss 01075 status svg https doi org 10 21105 joss 01075 binder https mybinder org badge svg https mybinder org v2 gh districtdatalabs yellowbrick develop filepath examples 2fexamples ipynb visual analysis and diagnostic tools to facilitate machine learning model selection banner docs images readme banner png https www scikit yb org en latest gallery html what is yellowbrick yellowbrick is a suite of visual diagnostic tools called visualizers that extend the scikit learn api to allow human steering of the model selection process in a nutshell yellowbrick combines scikit learn with matplotlib in the best tradition of the scikit learn documentation but to produce visualizations for your machine learning workflow for complete documentation on the yellowbrick api a gallery of available visualizers the contributor s guide tutorials and teaching resources frequently asked questions and more please visit our documentation at www scikit yb org https www scikit yb org installing yellowbrick yellowbrick is compatible with python 3 4 or later and also depends on scikit learn and matplotlib the simplest way to install yellowbrick and its dependencies is from pypi with pip python s preferred package installer pip install yellowbrick note that yellowbrick is an active project and routinely publishes new releases with more visualizers and updates in order to upgrade yellowbrick to the latest version use pip as follows pip install u yellowbrick you can also use the u flag to update scikit learn matplotlib or any other third party utilities that work well with yellowbrick to their latest versions if you re using anaconda recommended for windows users you can take advantage of the conda utility to install yellowbrick conda install c districtdatalabs yellowbrick using yellowbrick the yellowbrick api is specifically designed to play nicely with scikit learn here is an example of a typical workflow sequence with scikit learn and yellowbrick feature visualization in this example we see how rank2d performs pairwise comparisons of each feature in the data set with a specific metric or algorithm and then returns them ranked as a lower left triangle diagram python from yellowbrick features import rank2d visualizer rank2d features features algorithm covariance visualizer fit x y fit the data to the visualizer visualizer transform x transform the data visualizer show finalize and render the figure model visualization in this example we instantiate a scikit learn classifier and then use yellowbrick s rocauc class to visualize the tradeoff between the classifier s sensitivity and specificity python from sklearn svm import linearsvc from yellowbrick classifier import rocauc model linearsvc visualizer rocauc model visualizer fit x y visualizer score x y visualizer show for additional information on getting started with yellowbrick view the quick start guide https www scikit yb org en latest quickstart html in the documentation https www scikit yb org en latest and check out our examples notebook https github com districtdatalabs yellowbrick blob develop examples examples ipynb contributing to yellowbrick yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project large or small any contribution makes a big difference and if you ve never contributed to an open source project before we hope you will start with yellowbrick if you are interested in contributing check out our contributor s guide https www scikit yb org en latest contributing index html beyond creating visualizers there are many ways to contribute submit a bug report or feature request on github issues https github com districtdatalabs yellowbrick issues contribute a jupyter notebook to our examples gallery https github com districtdatalabs yellowbrick tree develop examples assist us with user testing https www scikit yb org en latest evaluation html add to the documentation or help with our website scikit yb org https www scikit yb org write unit or integration tests https www scikit yb org en latest contributing developing visualizers html integration tests for our project answer questions on our issues mailing list stack overflow and elsewhere translate our documentation into another language write a blog post tweet or share our project with others teach https www scikit yb org en latest teaching html someone how to use yellowbrick as you can see there are lots of ways to get involved and we would be very happy for you to join us the only thing we ask is that you abide by the principles of openness respect and consideration of others as described in the python software foundation code of conduct https www python org psf codeofconduct for more information checkout the contributing md file in the root of the repository or the detailed documentation at contributing to yellowbrick https www scikit yb org en latest contributing index html yellowbrick datasets yellowbrick gives easy access to several datasets that are used for the examples in the documentation and testing these datasets are hosted in our cdn and must be downloaded for use typically when a user calls one of the data loader functions e g load bikeshare the data is automatically downloaded if it s not already on the user s computer however for development and testing or if you know you will be working without internet access it might be easier to simply download all the data at once the data downloader script can be run as follows python m yellowbrick download this will download the data to the fixtures directory inside of the yellowbrick site packages you can specify the location of the download either as an argument to the downloader script use help for more details or by setting the yellowbrick data environment variable this is the preferred mechanism because this will also influence how data is loaded in yellowbrick note developers who have downloaded data from yellowbrick versions earlier than v1 0 may experience some problems with the older data format if this occurs you can clear out your data cache as follows python m yellowbrick download cleanup this will remove old datasets and download the new ones you can also use the no download flag to simply clear the cache without re downloading data users who are having difficulty with datasets can also use this or they can uninstall and reinstall yellowbrick using pip citing yellowbrick we would be glad if you used yellowbrick in your scientific publications if you do please cite us using the citation guidelines https www scikit yb org en latest about html citing yellowbrick affiliations district data labs docs images readme affiliates ddl png https districtdatalabs com numfocus affiliated project docs images readme affiliates numfocus png https numfocus org | machine-learning visual-analysis model-selection visualization scikit-learn visualizer matplotlib python estimator anaconda | ai |
Awesome-Korean-NLP | awesome korean nlp a curated list of natural language processing nlp of nlp of korean text nlp information written in korean feel free to contribute or blab it here http collabedit com ttqun maintainer jaemin cho https github com j min index 1 tools 1 tools 2 dataset 2 dataset 3 blogs slides researchers 3 blogs slides researchers 4 papers 4 papers 5 lectures 5 lectures 6 journals conferences institutes events 6 journals conferences institutes events 7 online communities 7 online communities 8 how to contribute 8 how to contribute 1 tools korean specific tools are listed ahead of language agnostic tools 1 1 morpheme part of speech pos tagger hannanum java c link https kldp net hannanum konlpy python link http konlpy org en v0 4 4 api konlpy tag module konlpy tag hannanum kkma java link http kkma snu ac kr documents index jsp paper http ids snu ac kr w images f f8 cpl2010 therocks pdf konlpy python link http konlpy org en v0 4 4 api konlpy tag module konlpy tag kkma komoran java link http www shineware co kr page id 835 konlpy python link http konlpy org en v0 4 4 api konlpy tag module konlpy tag komoran mecab ko c link https bitbucket org eunjeon mecab ko konlpy python link http konlpy org en v0 4 4 api konlpy tag mecab class twitter scala java link https github com twitter twitter korean text konlpy python link http konlpy org en v0 4 4 api konlpy tag module konlpy tag twitter net node js python ruby elasitc search bindings dparser rest api link http findyou readthedocs io ko latest dparser html utagger link http nlplab ulsan ac kr doku php id utagger arirang lucence java link http cafe naver com korlucene rouzeta link https shleekr github io slide http www slideshare net jieunlee5 ss 67333029 ref https readme skplanet com p 13166 video https www youtube com watch v tksvbfwzgn8 seunjeon scala java link https bitbucket org eunjeon seunjeon rhino link https sourceforge net projects koreananalyzer kts paper http scholar ndsl kr schdetail do cn npap07926299 link https ryubook wordpress com ea b9 9c ec a7 9d ec 83 88 1 5 5 beta 1 2 named entity ne tagger annie link https github com krikit annie 1 3 spell checker pnu spell checker link http speller cs pusan ac kr naver spell checker link https search naver com search naver where nexearch sm tab jum ie utf8 query ed 95 9c ea b8 80 eb a7 9e ec b6 a4 eb b2 95 ea b2 80 ec 82 ac ea b8 b0 daum spell checker link http alldic daum net grammar checker do hunspell ko link https github com changwoo hunspell dict ko 1 4 syntax parser dparser rest api link http findyou readthedocs io ko latest dparser html nlp hub java link http semanticweb kaist ac kr home index php nlp hub 1 5 sentimental analysis openhangul link http openhangul com paper http web yonsei ac kr dslab journal sentiment 20dictionary pdf 1 6 translator naver nmt link http labspace naver com nmt opennmt link http opennmt net google translator link https translate google com 1 7 packages konlp r link https cran r project org web packages konlp index html konlpy python link konlpy org paper http dmlab snu ac kr lucypark docs 2014 10 10 hclt pdf koalanlp scala link https nearbydelta github io koalanlp nltk python link http www nltk org paper http www aclweb org anthology p04 3031 gensim python link https radimrehurek com gensim fasttext c link https github com facebookresearch fasttext fasttext py python link https github com salestock fasttext py 1 8 others hangulpy python link https github com rhobot hangulpy hangulize python link https github com sublee hangulize hanja python link https pypi python org pypi hanja kroman link https github com zhangkaiyulw kroman hangul romanization ruby https github com cheunghy kroman gem python https github com zhangkaiyulw kroman py nodejs https github com cheunghy kroman js objective c https github com cheunghy kroman objc swift https github com cheunghy kroman swift hangul perl link https github com dragoncrane hangul hangul romanization textrankr python link https github com theeluwin textrankr demo https summariz3 herokuapp com textrank word2vec demo http virgon snu ac kr 8000 paper https docs google com viewer a v pid sites srcid zgvmyxvsdgrvbwfpbnwymde2agnsdhxnedozmjkyyjrkywvim2q0mzu2 word2vec analogy test link http badworddictionary xyz crowdsourced dic about badword in korean 2 dataset sejong corpus link https ithub korean go kr user corpus corpussearchmanager do kaist corpus link http semanticweb kaist ac kr home index php kaist corpus yonsei univ corpus korea univ corpus ulsan univ corpus link http nlplab ulsan ac kr doku php id ucorpus wikipedia dump link https dumps wikimedia org kowiki extractor https github com j min wikiextractor to the one text namuwiki dump link https namu wiki w eb 82 98 eb ac b4 ec 9c 84 ed 82 a4 eb 8d b0 ec 9d b4 ed 84 b0 eb b2 a0 ec 9d b4 ec 8a a4 20 eb 8d a4 ed 94 84 extractor https github com j min easy namuwiki extractor naver news archive link http dna naver com chosun archive link http srchdb1 chosun com pdf i archive naver sentiment movie corpus link https github com e9t nsmc sci news sum kr 50 link https github com theeluwin sci news sum kr 50 3 blogs slides researchers 3 1 blogs dsindex s blog link http dsindex github io link http exagen tistory com notice 63 beomsu kim word2vec link https shuuki4 wordpress com 2016 01 27 word2vec ea b4 80 eb a0 a8 ec 9d b4 eb a1 a0 ec a0 95 eb a6 ac cpuu google syntaxnet korean tranlsation of google blog http googleresearch blogspot kr 2016 05 announcing syntaxnet worlds most html link http cpuu postype com post 166917 theeluwin python crfsuite link http blog theeluwin kr post 147587579528 python crfsuite eb a5 bc ec 82 ac ec 9a a9 ed 95 b4 ec 84 9c ed 95 9c ea b5 ad ec 96 b4 ec 9e 90 eb 8f 99 eb 9d 84 ec 96 b4 ec 93 b0 ea b8 b0 eb a5 bc ed 95 99 ec 8a b5 ed 95 b4 eb b3 b4 ec 9e 90 jaesoo lim link https github com krikit hanal wiki ed 95 9c ea b5 ad ec 96 b4 ed 98 95 ed 83 9c ec 86 8c eb b6 84 ec 84 9d ea b8 b0 eb 8f 99 ed 96 a5 3 2 slides lucy park nltk gensim pycon apac 2015 link https www lucypark kr slides 2015 pyconkr jeongkyu shin building ai chat bot using python 3 tensorflow pycon apac 2016 link https speakerdeck com inureyes building ai chat bot using python 3 and tensorflow changki lee rnn nlp application kangwon univ machine learning course link http cs kangwon ac kr leeck ml rnn nlp pdf kyunghoon kim pycon apac 2016 link http www slideshare net koorukuroo 20160813 pycon2016apac hongjoo lee python 19 pycon apac 2016 link http www slideshare net hongjoo python 19 pycon apac 2016 kyumin choi word2vec pycon apac 2015 link http www slideshare net ssuser2fe594 2015 py con word2vec translated by hongbae kim link http www slideshare net ssuser06e0c5 ss 64417928 hongbae kim i link http www slideshare net ssuser06e0c5 i 64267027 changki lee link http www slideshare net deview f2 14341235 qid 12363290 1fe5 4903 9a5a 71a4e0c3842f v b from search 7 taeil kim daeneung son naver deview 2015 link http www slideshare net deview 242 52779038 4 papers 4 1 korean 2015 paper http www eiric or kr keydocs tmp fn 1512160195019 pdf link dead 4 2 english 5 lectures 5 1 korean lectures kangwon univ link http cs kangwon ac kr leeck nlp link https www datascienceschool net snu data mining business analytics link https www lucypark kr courses 5 2 english lectures stanford cs224n natural language processing link http web stanford edu class cs224n youtube https www youtube com playlist list pl6397e4b26d00a269 stanford cs224d deep learning for natural language processing link http cs224d stanford edu index html youtube https www youtube com playlist list plmimxx8char9ig0zhsytqgsdhb9weegam nltk with python 3 for nlp by sentdex youtube https www youtube com playlist list plqvvvaa0qudf2jswnfigklibinznic4hl lda topic models link https www youtube com watch v 3mhy4osyrf0 6 conferences institutes events 6 1 conferences link https sites google com site 2016hclt home kips link http www kips or kr link https ksss jams or kr co com egovmenu kci s url ac config guid acguidview kci guidid 000000001258 s menuid menu 000000000032000 s tabid 1 accnid ac0000000006 6 2 institutes link https sites google com site sighclt since 1989 link https sites google com site 2016hclt home since 2010 link http ithub korean go kr user contest contestintrolastview do link https sites google com site sighclt haengsasogae jayeon eon eocheoli tyutolieol link https sites google com site sighclt haengsasogae jayeon eocheoli mich jeongbogeomsaeg wokeusyab 1 link https ksss jams or kr co main jmmain kci 6 3 events contests link http ithub korean go kr user contest contestintrolastview do 7 online communities tensorflow kr facebook group link https www facebook com groups tensorflowkr ai korea facebook group link https www facebook com groups aikoreaopen bot group facebook group link https www facebook com groups botgroup facebook group link https www facebook com groups babelpish reddit machine learning top posts link https www reddit com r machinelearning top sort top t month 8 how to contribute 1 fork this repository by clicking on fork icon at the top right corner 2 get the link for the forked repo by clicking on the green button on your page something like https github com username awesome korean nlp git 3 on your local machine git clone https github com username awesome korean nlp git 4 cd awesome korean nlp 5 open readme md with your favorite text editor 6 edit 7 git commit a m added section 8 emoticons 8 git push and verify on your fork 9 goto https github com datanada awesome korean nlp and create pull request 10 compare across forks with base datanada awesome and head username awesome beginners guide https akrabat com the beginners guide to contributing to a github project | korean-nlp natural-language-processing nlp | ai |
ECE6680 | ece6680 embedded computing course description this course teaches the principles of using computing in the larger context of a system the student is expected to enter this class with an understanding of computer architecture assembler and proficiency programming in the c or related language emphasis is given to multimedia data images audio graphics as examples of processing found in an embedded system in concurrent lab work each student will design and implement many of the ideas that are found in a digital video camera | os |
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Cloud_Engineering | cloud engineering summary of clouding engineering concepts technologies and architectures what is the difinition why when used main architectures | cloud |
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minter-go-node | p align center background black img src minter logo svg width 400 p p align center a href https github com minterteam minter go node releases latest img src https img shields io github tag minterteam minter go node svg alt version a a href https github com moovweb gvm img src https img shields io badge go 1 15 blue svg alt go version a a href https github com minterteam minter go node blob master license img src https img shields io github license minterteam minter go node svg alt license a a href https github com minterteam minter go node commits master img src https img shields io github last commit minterteam minter go node svg alt last commit a a href https goreportcard com report github com minterteam minter go node img src https goreportcard com badge github com minterteam minter go node alt go report card a a href https github com minterteam minter go node actions img src https github com minterteam minter go node workflows docker badge svg alt github actions report card a a href https hub docker com r minterteam minter img alt docker pulls src https img shields io docker pulls minterteam minter a p minter is a global rewards and loyalty points network powered by a fast blockchain any brand community or blogger can create their own coins and launch their reward or loyalty system in minutes note this is alpha software please contact us if you intend to run it in production installation docker 1 grab latest docker compose save a href https raw githubusercontent com minterteam minter go node master docker compose yml docker compose yml a and run docker compose up d to run it in production we recommend to use bind host mount instead of volume 2 to build from source clone this repo make your changes and run docker compose up build d manual you can get official installation instructions in our docs https docs minter network section install minter 1 download minter node get latest binary build https github com minterteam minter go node releases suitable for your architecture and unpack it to desired folder 2 run minter node bash minter node resources documentation https docs minter network official site https minter network about minter blockchain https about minter network minter console https console minter network minter explorer https explorer minter network telegram bot wallet https t me bipwallet bot android wallet https play google com store apps details id network minter bipwallet related repositories minter go node docs https github com minterteam minter go node docs docs for minter node node grpc gateway https github com minterteam node grpc gateway grpc interface and swagger for node api v2 community telegram channel english https t me minterteam telegram channel russian https t me minternetwork telegram chat english http t me joinchat eafyerjsjzj nwh 139jlq telegram chat russian https t me joinchat eafyevd heoxdcv8yyaqng versioning semver minter uses semver http semver org to determine when and how the version changes according to semver anything in the public api can change at any time before version 1 0 0 to provide some stability to minter users in these 0 x x days the minor version is used to signal breaking changes across a subset of the total public api this subset includes all interfaces exposed to other processes but does not include the in process go apis upgrades in an effort to avoid accumulating technical debt prior to 1 0 0 we do not guarantee that breaking changes ie bumps in the minor version will work with existing blockchain in these cases you will have to start a new blockchain or write something custom to get the old data into the new chain however any bump in the patch version should be compatible with existing histories if not please open an issue https github com minterteam minter go node issues | blockchain golang cryptocurrency minter | blockchain |
oxygen-ui | h1 align center style color 343a40 margin 20px 0 img src https user images githubusercontent com 25959096 207556831 df3104cd f5bb 4e74 9cbe 226ebab20bac svg gh light mode only alt wso2 oxygen ui light mode logo img src https user images githubusercontent com 25959096 207556846 0e513a7c 2e59 413a 84ef d11f1de81247 svg gh dark mode only alt wso2 oxygen ui dark mode logo h1 p align center style font size 1 2rem the span style color 47ebd8 design system span powering a href https wso2 com wso2 a s core products p div align center a href https github com wso2 oxygen ui actions workflows release yml img src https github com wso2 oxygen ui actions workflows release yml badge svg alt release a a href https github com wso2 oxygen ui actions workflows deploy gh pages yaml img src https github com wso2 oxygen ui actions workflows deploy gh pages yaml badge svg alt deploy documentation a a href https github com wso2 oxygen ui actions workflows test runner yml img src https img shields io github actions workflow status wso2 oxygen ui test runner yml label f0 9f 8c b3 20unit 20tests alt unit tests a a href https github com wso2 oxygen ui actions workflows builder yml img src https img shields io github actions workflow status wso2 oxygen ui builder yml color red label f0 9f a7 b1 20builder alt builder a a href https stackoverflow com questions tagged wso2is img src https img shields io badge ask 20for 20help 20on stackoverflow orange alt stackoverflow a a href https discord gg wso2 img src https img shields io badge join 20us 20on discord 23e01563 svg alt discord a a href license img src https img shields io badge license apache 202 0 blue svg alt license a div br oxygen ui is the underlying design system that powers wso2 s core products like asgardeo choreo wso2 identity server etc this repository contains the source code of the key components that works together for building resilient uis packages package description version oxygen ui primitives packages primitives low level building blocks of oxygen ui e g icons fonts npm https img shields io npm v oxygen ui primitives color blue oxygen ui react packages react the react implementation of oxygen ui npm https img shields io npm v oxygen ui react color green oxygen ui react icons packages react icons react components for oxygen ui icons npm https img shields io npm v oxygen ui react icons color yellow oxygen ui logger packages logger logger for the oxygen ui packages npm https img shields io npm v oxygen ui logger color orange examples multi brand identity demo https wso2 github io oxygen ui examples multi brand identity sample app to showcase oxygen design system s multi branding capabilities features ability to switch between different wso2 brand identities i e asgardeo choreo etc ability to integrate with asgardeo branding https wso2 com asgardeo docs guides branding configure ui branding feature click here examples multi brand identity for the source code documentation for more information on how to use oxygen ui check out the documentation https wso2 github io oxygen ui website changelog you can find the latest changes and updates for oxygen ui in the changelog changelog md section this includes information on new features bug fixes and improvements made to the project with each release it s recommended to review the changelog before upgrading to a new version of oxygen ui contributing want to report a bug contribute some code or improve the documentation excellent read up on our guidelines for contributing contributing md to get started license licenses this source under the apache license version 2 0 license license you may not use this file except in compliance with the license | os |
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MPSoC-RTOS | mpsoc rtos queenfield queenfield master icon jpg distribution for a multi processor system on chip | os |
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salido-ios-challenge | installation you should be able to simply clone this repository and then install the pods see http cocoapods org for more details application overview whenever i dig into a codebase i always think there should be some light summary of how the application works code architecturally wise but i usually don t find one this is probably more verbose than i d normally put for an application i also included some notes about my design choices in italics data access all data access is performed via the abcapigateway class the gateway is accessed via the sharedinstance singleton method the apigateway is basically a wrapper around some afnetworking operations i could have chosen for the gateway to be abstract allowing us to swap out concrete implementations like loading from a local db or another api altogether but that seemed like overkill for this scenario cart the cart is a peristed set items that the user wants to buy it contains at a minimum the quantity of each item there is an abstract abccart protocol which defines the methods that all carts must provide the only concrete implementation for this is the abcmemorycart protocol though it is easy to add additional ones it seemed prudent to do this here i provided the simple in memory example but it could be easily extended to provide a coredata or user defaults for the lazy persistence that will survive between runs of the application or even it could be stored on the server to provide cart analytics additionally this is probably the most controversial of my decisions here but i could have implemented another data object for a cart item with gifting and discounts and such that would alomst certainly have to be done also it s convenient that i store the product object quantity so i retain all the titles etc without going back to the server but in a more complex scenario i would just keep product id as the key views and controllers the application is a basic splitviewcontroller with a abcproductlistviewcontroller showing all the items and an abcproductviewcontroller showing the details we use storyboards for expediency dependencies libextobjc provides easy access to weakify and strongify which imo everyone should be using to prevent retain cycles svpulltorefresh provides the infinite scrolling afnetworking for the usual misc i didn t have time but normally i set up an application wide error feedback view so that presenting errors is consistent across the app i find that to be super useful as the app gets more ungainly | os |
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UrbanXTools | urbanxtools language https github com caupdxurbanxlab urbanxtools blob main readme cn md engilish content add curve boundary in exposure rate 3d improve the efficiency of network analysis 3d content introduction introduction installation installation samples samples tools tools license license introduction image https github com caupdxurbanxlab urbanxtools blob main images intro 01 png 1 main functions autogeneration of spatial models by integrating urban planning logics and computer algorithms urbanxtools can rapidly generate rudimentary spatial models for urban design projects the spatial model will be complied with superior plans and regulations assessment of urban design projects urbanxtools can provide instant assessment of urban design projects based on multi disciplinary features and sustainable development goals construction of a feedback loop by using parametric design methods we aim to construct a mechanism for regulatory plans and urban design projects to get instant assessments feedbacks and modifications with this mechanism the efficiency and quality of urban design could be enhanced 2 objectives high quality sustainable urban development 3 developers tao yang weizhen luo xuhui lin chengru deng yufei dong 4 translating contributor yihan zhang 5 notifications rhino version 6 9 and above are recommended model unit must be meter and models cannot be generated if your model unit is millimeter mm please put your geometric objects such as roads and land parcels near the origin of coordinates 0 0 0 confined by computation accuracy of geometric objects in rhinocommon the geometric boolean operation might go wrong if you put your model too faraway from the origin point if you encounter any other problems or have any demands and suggestions please let us know via github issues we ll try our best to help installation download urbanxtools lanzou netdisk link https wwe lanzoui com b01tqy68f password h5d8 baidu netdisk link https pan baidu com s 1acbnxjod2pcm4wnlyhiozg password gz4u github link https github com caupdxurbanxlab urbanxtools releases 1 enter releases choose a compatible version download the zip file and extract please choose the latest version click to enter releases image https github com caupdxurbanxlab urbanxtools blob main images e5 ae 89 e8 a3 85 e6 b5 81 e7 a8 8b 000 png within the releases page please download full version urbanxtools v3 0 1 is recommended image https github com caupdxurbanxlab urbanxtools blob main images releaseversion png extract the zip file urbanxtools v3 0 1 image https github com caupdxurbanxlab urbanxtools blob main images e5 ae 89 e8 a3 85 e6 b5 81 e7 a8 8b 002 png 2 open grasshopper in rhino enter components folder open rhino and then grasshopper open file special folders components folder image https github com caupdxurbanxlab urbanxtools blob main images e5 ae 89 e8 a3 85 e6 b5 81 e7 a8 8b 003 png copy and paste the whole urbanxtools v3 0 1 folder here if you already have urbanxtools v 1 0 0 or urbanxtools v 2 0 0 in this folder please delete them in advance image https github com caupdxurbanxlab urbanxtools blob main images e5 ae 89 e8 a3 85 e6 b5 81 e7 a8 8b 003 1 png 3 check your urbanxtools v3 0 1 folder and then restart rhino make sure every file listed below are also in your urbanxtools v3 0 1 folder image https github com caupdxurbanxlab urbanxtools blob main images e5 ae 89 e8 a3 85 e6 b5 81 e7 a8 8b 004 png 4 finish if you have successfully installed urbanxtools you ll get this in your grasshopper image https github com caupdxurbanxlab urbanxtools blob main images e5 ae 89 e8 a3 85 e6 b5 81 e7 a8 8b 005 png samples 1 network structure image https github com caupdxurbanxlab urbanxtools blob main images 1 e7 a9 ba e9 97 b4 e7 bb 93 e6 9e 84 png sample networkanalysis https github com caupdxurbanxlab urbanxtools tree main samples 1 networkanalysis 2 aided regulatory planning image https github com caupdxurbanxlab urbanxtools blob main images 6 regulatoryplanning png sample aidedregulatoryplanning https github com caupdxurbanxlab urbanxtools tree main samples 6 regulatoryplanning 3 urban design autogeneration residential buildings image https github com caupdxurbanxlab urbanxtools blob main images 3 residentalautogeneration png commercial buildings warehouses factories image https github com caupdxurbanxlab urbanxtools blob main images 4 busniessautogeneration png sample urbandesign singlesite https github com caupdxurbanxlab urbanxtools tree main samples 3 urbandesign sample urbandesign multisitessites https github com caupdxurbanxlab urbanxtools tree main samples 3 urbandesign 4 coverage analysis of public facilities image https github com caupdxurbanxlab urbanxtools blob main images 2 facilitycoverage png sample facilitylocation https github com caupdxurbanxlab urbanxtools tree main samples 2 facilitylocation 5 sustainability analysis of urban design projects image https github com caupdxurbanxlab urbanxtools blob main images 7 resourcedemand png sample resources demand https github com caupdxurbanxlab urbanxtools tree main samples 4 resourcesdemand 6 exposure rate analysis of urban design projects 2d image https github com caupdxurbanxlab urbanxtools blob main images 8 exposure png sample exposurerates2d https github com caupdxurbanxlab urbanxtools blob main samples 5 spatialanalysis 7 exposure rate analysis of urban design projects 3d image https github com caupdxurbanxlab urbanxtools blob main images e6 9b 9d e5 85 89 e7 8e 873d png sample exposurerates3d https github com caupdxurbanxlab urbanxtools blob main samples 5 spatialanalysis 8 exposure rate analysis of urban design projects 3d mesh image https github com caupdxurbanxlab urbanxtools blob main images e6 9b 9d e5 85 89 e7 8e 873d mesh png sample exposurerates3d mesh https github com caupdxurbanxlab urbanxtools blob main samples 5 spatialanalysis 9 visual graph network analysis of urban design projects image https github com caupdxurbanxlab urbanxtools blob main images vissyntax png sample exposurerates3d visualgraphnetwork https github com caupdxurbanxlab urbanxtools blob main samples 5 spatialanalysis 10 extract centroidline of road network image https github com caupdxurbanxlab urbanxtools blob main images 9 extractcentroidline png centroidlineextraction https github com caupdxurbanxlab urbanxtools tree main samples 7 centroidlineextraction tools 1 network structure network roadssplitter purpose clean and split the curves to handle degeneracies such as overlaps identical shapes invalid curves etc input original curves output cleaned and split curves sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px na roadssplitter png networkstructure computing3d purpose this command computes network properties including angular distance and metric distance in space syntax you may choose different radius for calculation betweenness within a certain distance this command finds the shortest paths between any pair of points and counts the number of times each road segment is passed closeness within a certain distance compute the average distance from this point to a specified destination point along the shortest paths input road network of 2d or 3d geometries radius merging or not output metric choice metric integration metric mean depth metric total depth angular choice angular integration angular mean depth angular total depth normalized angular choice nach normalized angular integration nain cleaned road segments sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px na computing png networkstructure siteaccessibility purpose this command computes the accessibility scores of sites input cleaned road segments accessibility scores of each road segment curves of site boundaries output polygons generated by site boundaries accessibility scores of sites with the same sequence as polygons above sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px na siteaccessibility png networkstructure roaddensity purpose compute road density within the given boundary input roads to be computed boundary output road density numeric data road density unit km km road segments cookie cut intersected by the given boundary sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px na roaddensity png 2 regulatory planning regulatoryplanning generatingsites purpose automatically generate sites polygon based on the input road network input a list of primary roads a list of secondary roads optional a list of tertiary roads optional a list of branch roads optional width of roads in the order from primary roads to branch roads radius for rounding the site corners the boundary within which sites will be generated output all generated site polygons sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px na generatingsites png regulatoryplanning clusteringblocks purpose clustering a set of sites based on the shortest path distance matrix by using hierarchical agglomerative clustering hac algorithm input roads sites diameters for each clustering levels output links for visualizing the hierarchical relationships among the clusters id for each site in the tree structure to represent the hierarchy of all clusters an object containing all the information of the clustering results to be used in the regulatoryplanning landuseallocation component sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px na clusteringblocks png regulatoryplanning clusteringpoints purpose clustering a set of sites based on the euclidean distance matrix among all centroids of the sites by using hierarchical agglomerative clustering hac algorithm input centroids of each site diameters for each clustering levels output links for visualizing the hierarchical relationships among the clusters id for each site in the tree structure to represent the hierarchy of all clusters an object containing all the information of the clustering results to be used in the regulatoryplanning landuseallocation component sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px na clusteringpoints png regulatoryplanning landuseallocation purpose automatically allocating landuse to each of the sites input sites accessibility scores with different radius for each site recommended 3 branches the object containing all the information of clustering results which is obtained from the regulatoryplanning clusteringblocks component landuse structure with the order representing their priority and their values indicating their proportion of area among all the sites respectively format r 0 2 output sites wihch may be split after the landuse allocation to fulfill the requirement of landuse structure allocated landuse for each site with the same sequence as the sites above actual landuse structure the object containing all the results of landuse allocation which is useful for the regulatoryplanning farallocation component sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rp landuseallocation png regulatoryplanning farallocation purpose automatically allocating floor area ratio far to each of the sites input the object containing all the results of landuse allocation obtained from the regulatoryplanning landuseallocation component total building area for the overall urban design building area structure represented by the percentage of building area of each category of landuse output allocated far for each site sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rp farallocation png 3 urban design urbandesign siteparameter purpose this command autogenerates site parameters based on roads accessibility scores and site info input cleaned roads accessibility scores for each road segment curves of site boundaries output initial parameters for all the sites sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px ud siteparameter png urbandesign siteparameterextra purpose this command allows you to adjust site parameters such as far and building density input initial parameters of all sites obtained from the urbandesign siteparameter component landuse type r 0 c 1 gic 2 m 3 w 4 far density mixed ratio building styles for residential buildings 0 rows 1 dots for non residential buildings 0 dots 1 groups 2 mixed orientation of buildings unit radians output adjusted parameters for each site sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px ud siteparameterextra png urbandesign sitegenerateplans purpose this command autogenerates building entities input adjusted parameters for all sites obtained from urbandesign siteparameterextra component city id output the object containing results of the design which is useful for the sustainability analysis module curves of original site boundaries not split fars building densities subsite boundaries setback boundaries for each site outline curves of the floor layer of each building outline curves of each building roof numbers of levels of each building brep geometries of each building functions of each brep of each building corresponding to building breps sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px ud sitegenerateplans png 4 facility analysis facilityanalysis connecttonetwork purpose connect all sites to their nearest road segments input cleaned road network sites output generic parameters which is useful for the facilityanalysis coveragearea component new road segments after computing the connection sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px fl connecttonetwork png facilityanalysis coveragearea purpose compute facilities coverage area by shortest path distances in the road network based on a given radius input generic parameters obtained from the facilityanalysis connecttonetwork component sites where public facilities locate service radius of the facilities which is defined as shortest paths within the road network output sites within facilities coverage area centroids of all covered sites sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px fl coveragearea png 5 sustainability analysis sustainabilityanalysis energy purpose compute the energy consumption for each building in the autogenerated plan input generic parameters containing the original results of the design obtained from urbandesign sitegenerateplans component output energy consumption of each building sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rd energy png sustainabilityanalysis energycarbonemissions purpose compute the carbon emissions for generating the given amount of energy input energy consumption unit kwh year output carbon emissions for generating the given amount of energy unit tco2 year sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rd e carbonemissions png sustainabilityanalysis water purpose compute the water consumption for each building in the autogenerated plan input generic parameters containing the original results of the design obtained from urbandesign sitegenerateplans component output water consumption of each building sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rd water png sustainabilityanalysis waste purpose compute waste production for each building in the autogenerated plan input generic parameters containing the original results of the design obtained from urbandesign sitegenerateplans component output waste production of each building sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rd garbage png sustainabilityanalysis wastecarbonemissions purpose compute the carbon emissions for waste treatment input domestic waste generation which requires further treatment unit tons year method of waste treatment 0 waste landfill 1 waste incineration 2 waste composting output carbon emissions for waste treatment unit tons co2 year sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rd g carbonemissions png sustainabilityanalysis energycustom purpose compute energy consumption of buildings based on the custom input input building breps building functions of each brep output energy consumption for each building sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rd energycustom png sustainabilityanalysis watercustom purpose compute water consumption of buildings based on the custom input input building breps building functions of each brep output water consumption for each building sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rd watercustom png sustainabilityanalysis wastecustom purpose compute waste production of buildings based on the custom input input building breps building functions of each brep output waste production for each building sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rd garbagecustom png sustainabilityanalysis population purpose estimated population of each residential building input building breps output estimated population of each building sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px rd population png 6 visibility analysis visibilityanalysis exposurerate2d purpose by analyzing its relationship to road intersections this command returns 2d exposure rate for ground floor commerce which could be a quantified spatial feature considered in commercial site selection process input building breps preset view points normalize or not radius of viewshed subdivision higher value will lead to higher accuracy but also longer runtime output outlines of building breps exposure rate for brep outlines sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px sa exposurerate2d png visibilityanalysis generatemeshbuilding purpose convert buildings from rhino brep to dmesh according to subdivision this command aims to improve computational efficiency input rhino brep of buildings subdivision default as 1 higher value will lead to higher accuracy but also longer runtime output generatemesh class sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px sa generatemesh png visibilityanalysis generatemesh purpose convert geometry from rhino mesh to dmesh this command aims to improve computational efficiency input mesh output generatemesh class sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px sa generatemesh mesh png visibilityanalysis exposurerate3dbuildings purpose by analyzing its relationship to view points this command returns 3d exposure rate for buildings which could be a quantified spatial feature considered in commercial site selection process input preset view points generatemesh class view range in each point as radius default as 300 output colored side mesh of each building uncolored top mesh and bottom mesh count of each mesh got intersected exposure rates for each view point sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px sa exposurerate3d png visibilityanalysis exposurerate3dmesh purpose by analyzing its relationship to view points this command returns 3d exposure rate for mesh entities which could be a quantified spatial feature considered in commercial site selection process input preset view points generatemesh class view range in each point as radius default as 300 output colored mesh count of each mesh got intersected exposure rates for each view point sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px sa exposurerate3d mesh png visibilityanalysis visualcalc purpose compute the visibility of road network by interpolating points in 3d context input preset road network generatemesh class subdivision of roads to be divided unit meter default 500m view range in each point as radius default as 300 output mesh or not output cleaned roads which is useful in the visibilityanalysis visualsyntaxcomputing component viewpoints on roads visibility properties of each road colored mesh depending on whether you choose to output mesh sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px sa visualcalc png visibilityanalysis visualsyntaxcomputing purpose compute visual graph network analysis by input road network and corresponding scores in 3d context which could be used to find out the most informative route thus this application is called 3d informative visualization input cleaned roads obtained from the visibilityanalysis visualcalc component corresponding scores of each road radius default as 1 which leads to global calculation output visual total depth visual mean depth visual integration visual choice normalized visual integration normalized visual choice sample image https github com caupdxurbanxlab urbanxtools blob main images logo 24px sa visualsyntaxcompute png 6 waterpipenetwork calculation coming soon details summary waterpipenetwork parameters summary water inpfiletogeometry purpose input output sample water calculatesitedemand purpose input output sample water networkoptimization purpose input output sample details license gnu general public license v 3 copyright c 2020 tao yang weizhen luo xuhui lin chengru deng this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 3 of the license or at your option any later version this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details | parametric-design sustainability urban-design | os |
ESSD-110.2-PA2 | essd 110 2 pa2 embedded system software design project 2 | os |
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iot-inspector-client | iot inspector 2 what is iot inspector an open source tool for capturing analyzing and visualizing the network activities of your smart home devices it does not require special hardware or changes to your network simply run iot inspector and you ll see the results instantly you can use iot inspector to learn what companies countries your iot devices communicate with how much data your devices are sending and receiving discover any unknown devices on the network maybe more tell us please iot inspector is a research project by researchers from new york university see our main website https inspector engineering nyu edu and documentation https github com nyu mlab iot inspector client wiki for more information also see screenshots https github com nyu mlab iot inspector client wiki screenshots windows running iot inspector download windows 10 11 download the installer https github com nyu mlab iot inspector client wiki download install 64 bit intel cpu only macos and linux run from source code https github com nyu mlab iot inspector client wiki download install macos installer binary coming soon sign up for our mailing list https forms gle yrmxox64hhtpqynq6 to receive updates about upcoming releases and research findings overall device activities https github com nyu mlab iot inspector client assets 1479070 671fdea4 4c8d 405e 84d6 ad1b71f4356e what s new in version 2 we released version 1 in 2019 since then we have had 7k users who have donated the network traffic from 65k devices we recently launched version 2 with the following changes revamped ui ux decoupled from cloud you can run iot inspector without donating data to us e g in sensitive enterprise environments extensibility for developers more details to come see this article https www usenix org publications loginonline three years crowdsourcing smart home network traffic about our experience maintaining iot inspector in the last few years media coverage see articles about iot inspector in the press all paywalls removed new york times https www nytimes com 2020 01 07 opinion location tracking privacy html unlocked article code bxakhvwovzlmxytvb6yr5pjjomrjaoerarcon dhsv 6ryopm7lpp1zna bmq9dnbbukm 1qwxh l7nhm1 dlm5pzplmf 6o igoboxpyqwyas3mtp hc0gszv jqyidikzd4fqjbzxxpidy9v9fhkfdovuyscgbqoklwod3cramke80pqapj6 m6du5tqsrpoiiv0gjrro9tfnxj6pswpukhxy5slih34qyixu81js 9lwmgbtr7bruwzidjtt0wb4syrjoiyxykxd4lsm1thhjlr8bufaq b75w 3zfhanpiegh4nokdsb0lqbkv3mo0yatl2 cuepqh5xl8zoza6bou smid url share why you should take a close look at what tracks you it might help you manage your privacy national public radio https www sciencefriday com segments smart tv roku spying your smart tv is watching you washington post http web archive org web 20200727193548 https www washingtonpost com technology 2019 09 18 you watch tv your tv watches back noredirect on you watch tv your tv watches back gizmodo https gizmodo com this simple tool will reveal the secret life of your sm 1832264323 this simple tool will reveal the secret life of your smart home techcrunch https techcrunch com 2019 04 13 spy on your smart home with this open source research tool guccounter 1 guce referrer ahr0chm6ly9pbnnwzwn0b3iuzw5naw5lzxjpbmcubnl1lmvkds8 guce referrer sig aqaaaisgyigmozw6fpw gf03ki87lghe7mgp f27fm5ewtilu26rprxdvj vq bwcaaupgfg2ajgvddurktvx92tytf7seelflgpa paq6vngpddbxu3vemk4uczqjky7tuoiky1w685d 5o6 u7ifym9n2kebqkjtobuwusdgijz65y spy on your smart home with this open source research tool getting started download and install see this page https github com nyu mlab iot inspector client wiki download install if you want to run our precompiled binaries running from the source code you will need python and git already set up on your system you ll also need to be familiar with terminals tested on macos ventura run the following in your terminal git clone https github com nyu mlab iot inspector client git cd iot inspector client python3 m venv env source env bin activate pip install r requirements general txt to run do the following source env bin activate cd ui start bash tested on windows 10 11 with python 3 8 run the following in your terminal git clone https github com nyu mlab iot inspector client git cd iot inspector client python exe m venv env env script activate bat pip install r requirements txt if you have a more modern version of python say python 3 11 try replacing the last line above with pip install r requirements general txt to run do the following in an terminal with administrator s priviledge env script activate bat cd ui streamlit exe run device list py server port 33761 browser gatherusagestats false server headless true server baseurlpath inspector dashboard developing for iot inspector to learn how inspector scans the network and captures the traffic look at the core start py file the relevant modules include arp scanner py arp spoofer py and packet py to learn how inspector constructs the user interface follow the ui start bash command for details see our documentation https github com nyu mlab iot inspector client wiki questions we are still revising the documentation and fixing bugs for iot inspector 2 we d love to hear from you here s how to contact us https github com nyu mlab iot inspector client wiki contact us also here s more information https github com nyu mlab iot inspector client wiki frequently asked questions about iot inspector about the iot inspector team | server |
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laravel-NoAPI | laravel noapi it is a laravel package for people who are interested to develop mobile apps but do not have experience with backend they don t want to spend much for the development of apis looking for some automated but secure apis | server |
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NaturalLanguageProcessing | cran task view natural language processing url https cran r project org view naturallanguageprocessing source file naturallanguageprocessing md naturallanguageprocessing md contributions suggestions and improvements for this task view are very welcome and can be made through issues or pull requests here on github or via e mail to the maintainer address for further details see the contributing https github com cran task views ctv blob main contributing md guide all contributions must adhere to the code of conduct https github com cran task views ctv blob main codeofconduct md | r rstats cran task-views natural-language-processing text-mining | ai |
NLP-Corpora | nlp corpora this is a list of nlp corpora you can report a new corpus at the issues page toc depthfrom 1 depthto 6 withlinks 1 updateonsave 1 orderedlist 0 nlp corpora nlp corpora emoji meanings emoji meanings corpora corpora dialog task oriented dialog task oriented dialog others dialog others question answering question answering translation translation sentiment analysis sentiment analysis recommendation recommendation others others references references toc emoji meanings machine to machine conversations which were synthetically generated human to human conversations including dialogs by crowd workers such as mechanical turk human to machine dialog systems conversations written texts utterance is not assumed spoken dialogs also containing assuming to speak generally written dialogs by crowd workers the data was collected using a wizard of oz https en wikipedia org wiki wizard of oz experiment scheme corpora dialog task oriented permuted dialog babi tasks ibm emnlp 2018 https github com ibm permuted babi dialog tasks 28 silence what do you think of this option resto rome expensive french 8stars 1 what do you think of this option resto rome expensive french 8stars 2 29 do you have something else sure let me find an other option for you 30 silence what do you think of this option resto rome expensive french 8stars 2 31 do you have something else sure let me find an other option for you 32 silence what do you think of this option resto rome expensive french 1stars 33 that looks great great let me do the reservation multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling emnlp 2018 https arxiv org pdf 1807 06517 pdf 2480 single domain dialogs and 7375 multi domain dialogs usually from 2 up to 5 domains this dataset is designed for dialog state tracking there are code https github com nmrksic neural belief tracker and corpus http dialogue mi eng cam ac uk index php corpus 3 domain s bus hotel train usr i need to book a hotel in the east that has 4 stars sys i can help you with that what is your price range usr that doesn t matter as long as it has free wifi and parking sys if you d like something cheap i recommend the allenbell for something moderately priced i would recommend the warkworth house usr could you book the wartworth for one night 1 person sys what day will you be staying usr friday and can you book it for me and get a reference number sys booking was successful reference number is bmukptg6 can i help you with anything else today usr i am looking to book a train that is leaving from cambridge to bishops stortford on friday sys there are a number of trains leaving throughout the day what time would you like to travel usr i want to get there by 19 45 at the latest sys okay the latest train you can take leaves at 17 29 and arrives by 18 07 would you like for me to book that for you usr yes please i also need the travel time departure time and price sys reference number is uifv8fas the price is 10 1 gbp and the trip will take about 38 minutes may i be of any other assistance usr yes sorry but suddenly my plans changed can you change the wartworth booking to monday for 3 people and 4 nights sys i have made that change and your reference number is yf86ge4j usr thank you very much goodbye sys you re welcome have a nice day cmu simdial synthetic task oriented dialog generator with controllable complexity sigdial 2018 https github com snakeztc simdial simdial is a multi domain dialog generator chich can generate conversations for slot filling domains with configurable complexity domain restaurant sys welcome to the restaurant recommendation system what do you need user 0 83 hello robot i am looking for a restaurant sys which city are you interested in user 0 75 i am in san jose and korean uhm sorry i am interested in chinese food sys i believe you said san jose i believe you said chinese sys query goal restaurant food pref chinese location san jose kb ret restaurant 17 sys restaurant 17 is a good choice what else can i do for you user 0 6 not done yet is that restaurant open now sys query goal open restaurant 17 food pref chinese location san jose kb ret open yes sys yes it is open right now what else user 0 69 no the 6 dialog babi tasks facebook 2016 https research fb com downloads babi the set of six tasks for testing e2e task oriented dialogs systems for restaurant reservation task1 5 are synthetically generated data while task6 is created from human to computer dialogs dstc2 for each task there are 1000 dialogs for training 1000 for development and 1000 for testing u silence s hello welcome to the cambridge restaurant system you can ask for restaurants by area price range or food type how may i help you u cheap restaurant that serves german food s i m sorry but there is no restaurant serving german food u what about asian oriental foo s you are looking for a german restaurant right u asian s let me confirm you are looking for a restaurant in the cheap price range right babi plus heriot watt 2017 https arxiv org abs 1709 07840 synthetically added disfluencies to the dialogs of babi task 1 and their code https github com ishalyminov memn2n is also available sys hello what can i help you with today usr i d like to book a uhm yeah i d like to book a table in a expensive price range no sorry in a cheap price range sys i m on it any preference on a type of cuisine usr with indian food no sorry with spanish food a new multi turn multi domain task oriented dialogue dataset stanford 2017 https nlp stanford edu blog a new multi turn multi domain task oriented dialogue dataset three domains weather calendar poi dialogs of a car assistant 3 031 multi turn dialogues are created in manner of wizard of oz driver i need to find the time and parties attending my optometrist appointment car i have 3 appointments scheduled with alex your sister and jeff which are you referring to driver i want to know about the one that alex is joining me at car that optometrist appointment is at 4 pm driver thanks car no problem frames a corpus for adding memory to goal oriented dialogue systems maluuba 2017 https datasets maluuba com frames a corpus of 1369 human human dialogues with an average of 15 turns per dialog collected in wizard of oz user i d like to book a trip to boston from london on saturday august 13 2016 for 8 adults i have a tight budget of 1700 frame 1 act 1 inform intent book act 2 inform dst city boston or city london str date saturday august 13 2016 n adults 8 budget 1700 wizard hi i checked a few options for you and unfortunately we do not currently have any trips that meet this criteria would you like to book an alternate travel option frame 1 act 1 no result act 2 suggest dst city user yes how about going to detroit from london on august 13 2016 for 5 adults for this trip my budget would be 1900 frame 2 act 1 inform dst city detroit n adults 5 budget 1900 ref 1 or city london str august 13 2016 wizard i checked the availability for those dates and there were no trips available would you like to select some alternate dates frame 2 act 1 no result str date end date act 2 suggest str date end date dstc 1 6 https www microsoft com en us research event dialog state tracking challenge dialog state tracking challenge is research community tasks the link presents each dstc task respectively dstc1 bus schedules dstc2 3 restaurant reservations dstc4 tourist information dstc5 tourist information and privided in several languages dstc6 it consists of 3 parallel tracks end to end goal oriented dialog learning end to end conversation modeling and dialogue breakdown detection dialog others multimodal emotionlines dataset 2018 https affective meld github io meld is a multi modal audio vision text with an emotion dataset using friends tv shows it has more than 1300 dialogues and 13000 utterances img src https raw githubusercontent com jojonki nlp corpora master img meld jpeg width 500 document grounded conversations emnlp 2018 https github com festvox datasets cmu dog conversations that are about the contents of a specified document the documents were wikipedia articles about movies the dataset contains 4112 conversations with an average of 21 43 turns per conversation user2 hey have you seen the inception user1 no i have not but have heard of it what is it about user2 it s about extractors that perform experiments using military technology on people to retrieve info about their targets personalizing dialogue agents facebook 2018 https github com facebookresearch parlai tree master projects personachat two people dialogs conditioned on personas this contains 164 356 utterances 10 981 dialogs person 1 hi person 2 hello how are you today person 1 i am good thank you how are you person 2 great thanks my children and i were just about to watch game of thrones person 1 nice how old are your children person 2 i have four that range in age from 10 to 21 you person 1 i do not have children at the moment person 2 that just means you get to keep all the popcorn for yourself person 1 and cheetos at the moment person 2 good choice do you watch game of thrones person 1 no i do not have much time for tv person 2 i usually spend my time painting but i love the show edina building an open domain socialbot with self dialogues 2017 https github com jfainberg self dialogue corpus the data is collected by amt in manner of self dialog and used in alexa prize 2017 the workers created self dialogs alone given a topic there are 24 283 self dialogues 3 653 313 words across 141 945 turns from 2 717 workers what is your absolute favorite movie i think beauty and the beast is my favorite the new one no the cartoon something about it just feels magical it is my favorite disney movie what s your favorite movie in general i think my favorite is the sound of music really other than cartoons and stuff i can never get into musicals i love musicals i really liked phantom of the opera dailydialog a manually labelled multi turn dialogue dataset 2017 http yanran li dailydialog the dialogs are our daily communication way and cover various topics about our daily life the dataset contains 13 118 multi turn dialogs a i m worried about something b what s that a well i have to drive to school for a meeting this morning and i m going to end up getting stuck in rush hour traffic b that s annoying but nothing to worry about just breathe deeply when you feel yourself getting upset the ubuntu dialogue corpus 2015 https github com rkadlec ubuntu ranking dataset creator human to human dialogs in ubuntu boards 1 million multi turn dialogues with a total of over 7 million utterances and 100 million words question answering commonsenseqa 2018 https www tau nlp org commonsenseqa commonsenseqa is a new qa dataset that contains 9 500 examples and aims to test commonsense knowledge open book question answering 2018 http data allenai org openbookqa openbookqa modeled after open book exams for assessing human understanding of a subject the open book that comes with our questions is a set of 1326 elementary level science facts roughly 6000 questions probe an understanding of these facts and their application to novel situations this requires combining an open book fact e g metals conduct electricity with broad common knowledge e g a suit of armor is made of metal obtained from other sources question which of these would let the most heat travel through a a new pair of jeans b a steel spoon in a cafeteria c a cotton candy at a store d a calvin klein cotton hat science fact metal is a thermal conductor common knowledge steel is made of metal heat travels through a thermal conductor coqa 2018 https stanfordnlp github io coqa coqa is a large scale dataset for building conversational question answering systems it contains 127k questions with answers obtained from 8k conversations each conversation is collected by paring two crowdworkers to chat about a passage in the form of qas jessica went to sit in her rocking chair today was her birthday and she was turning 80 her granddaughter annie was coming over in the afternoon and jessica was very excited to see her her daughter melanie and melanie s husband josh were coming as well jessica had q1 who had a birthday a1 jessica r1 jessica went to sit in her rocking chair today was her birthday and she was turning 80 q2 how old would she be a2 80 r2 she was turning 80 squad 2 0 2018 https rajpurkar github io squad explorer squad2 0 combines the 100 000 questions in squad1 1 with over 50 000 new unanswerable questions written adversarially by crowdworkers to look similar to answerable ones spoken squad 2018 https github com chiahsuan156 spoken squad artificially generated corpus based on squad by using google tts they used google tts to generate the spoken version of squad articles then the generated texts are passed to cmu sphinx speech recognizer to validate whether the generated texts contain a correct answer odsqa 2018 https github com chiahsuan156 spoken squad this is a chinese corpus over three thousand questions by 20 chinese speakers translation sentiment analysis recommendation redial microsoft research nips 2018 https redialdata github io website a large scale data set consisting of real world dialogues centered around movie recommendations redial consists of over 10k conversations centered around the theme of providing movie recommendations seeker those sound good i m going to look into those movies human i hope you enjoy have a nice one hred have you seen foxcatcher it s about a man who has a rich guy ours i hope i was able to help you find a good movie to watch seeker thank you for your help have a great night good bye others wikihow 2018 https github com mahnazkoupaee wikihow dataset wikihow is a new large scale dataset using the online wikihow http www wikihow com knowledge base each article consists of multiple paragraphs and each paragraph starts with a sentence summarizing it by merging the paragraphs to form the article and the paragraph outlines to form the summary the resulting version of the dataset contains more than 200 000 long sequence pairs spider 1 0 emnlp 2018 https yale lily github io spider a human labeled dataset for complex and cross domain semantic parsing and text to sql task question what are the name and budget of the departments with average instructor salary greater than the overall average sql select t2 name t2 budget from instructor as t1 join department as t2 on t1 department id t2 id group by t1 department id having avg t1 salary select avg salary from instructor youtube av 50k an annotated corpus for comments in autonomous vehicles 2018 https arxiv org abs 1807 11227 comments from 50k youtube videos related to autonomous vehicles the total of comments is 30 456 including 19 126 annotated comments swag a large scale adversarial dataset for grounded commonsense inference 2018 https rowanzellers com swag swag situations with adversarial generations is a dataset for grounded commonsense inference it consists of 113k multiple choice questions about grounded situations on stage a woman takes a seat at the piano she a sits on a bench as her sister plays with the doll b smiles with someone as the music plays c is in the crowd watching the dancers d nervously sets her fingers on the keys a girl is going across a set of monkey bars she a jumps up across the monkey bars b struggles onto the monkey bars to grab her head c gets to the end and stands on a wooden plank d jumps up and does a back flip friends tv corpus 2011 https sites google com site friendstvcorpus the data was created for an analysis of the use of various linguistic structures and a comparison of their use in inter gender and intra gender conversation environments in friends the paper s title is a study into the use of linguistic structures used inter gender and intra gender in the tv show friends 1 1 monica f monica there s nothing to tell he s just some guy i work with there s nothing to tell he s just some guy i work with there ex s vbz nothing pn1 to to tell vvi he pphs1 s vbz just rr some dd guy nn1 i ppis1 work vv0 with iw 0101 txt 101 1 joey m joey c mon you re going out with the guy there s gotta be something wrong with him c mon you re going out with the guy there s gotta be something wrong with him c m vv0 on rp you ppy re vbr going vvg out rp with iw the at guy nn1 there ex s vhz got vvn references a survey of available corpora for building data driven dialogue systems https breakend github io dialogdatasets natural language processing corpora nlp for hackers https nlpforhackers io corpora | ai |
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exonum | exonum status ci https github com exonum exonum actions workflows ci yml badge svg branch master https github com exonum exonum actions workflows ci yml dependency status https deps rs repo github exonum exonum status svg https deps rs repo github exonum exonum codecov https codecov io gh exonum exonum branch master graph badge svg https codecov io gh exonum exonum project info docs rs https docs rs exonum badge svg https docs rs exonum license apache 2 0 https img shields io github license exonum exonum svg license md loc https tokei rs b1 github exonum exonum https github com exonum exonum rust 1 55 0 required https img shields io badge rust 1 55 0 blue svg label required 20rust community join the chat at https gitter im exonum exonum https img shields io gitter room exonum exonum svg label chat https gitter im exonum exonum join the chat at https t me exonum blockchain https img shields io badge chat on 20telegram brightgreen svg https t me exonum blockchain join the chat at https gitter im exonum ruexonum https img shields io gitter room exonum ruexonum svg label russian 20chat https gitter im exonum ruexonum join the chat at https t me exonumru https img shields io badge russian 20chat on 20telegram brightgreen svg https t me exonumru website https img shields io website http exonum com svg label website https exonum com exonum https exonum com is an extensible open source framework for creating blockchain applications exonum can be used to create cryptographically powered distributed ledgers in virtually any problem domain including fintech govtech and legaltech the exonum framework is oriented towards creating permissioned blockchains that is blockchains with the known set of blockchain infrastructure providers if you are using exonum in your project and want to be listed on our website github list write us a line to contact exonum com contents this is the main exonum repository containing the bulk of rust crates used in exonum rust crates for exonum are intended to be reasonably small and reusable hence there is relatively large number of them main crates core library exonum readme md node implementation exonum node readme md node cli cli readme md upstream dependencies cryptographic library components crypto readme md database backend for merkelized data structures components merkledb readme md key management components keys readme md derive macros components derive readme md protobuf helpers components proto readme md protobuf support for build scripts components build readme md high level http api abstraction components api readme md tools for building services rust runtime runtimes rust readme md testing framework test suite testkit readme md services and node plugins explorer service services explorer readme md and explorer library components explorer readme md middleware service services middleware readme md supervisor service services supervisor readme md time oracle service services time readme md system api plugin components system api readme md examples cryptocurrency examples cryptocurrency readme md advanced cryptocurrency examples cryptocurrency advanced readme md timestamping examples timestamping readme md sample runtime implementation examples sample runtime readme md versioning policy exonum crates follow semantic versioning https semver org the exonum crate and its re exported dependencies exonum crypto exonum merkledb and exonum keys are released at the same time their version is considered the version of the exonum framework on the other hand the crates downstream of exonum e g exonum node or independent of it e g exonum api may evolve at different speeds including major releases not tied to a major exonum release throughout the exonum codebase certain apis are described in the api docs as unstable or experimental such apis may be removed or changed in a semantically non breaking release for example a minor release of the corresponding crate similarly nominally public apis that are hidden from the docs via doc hidden are considered unstable and thus exempt from semantic versioning limitations supported rust versions the exonum crates are built against a specific stable rust version 1 45 0 newer stable versions are supported as a result feel free to file an issue if any exonum crate does not build on a newer stable version newer beta and nightly versions should be supported as well but no specific effort is allocated into supporting them due to at least some external dependencies not factoring the minimum supported rust version into their semantic versioning policy the exonum crates effectively have no choice but to do the same namely a bump of the minimum supported rust version will not be considered a semantically breaking change it is however guaranteed that the exonum crates will build on some stable rust note that due to versioning policies of external dependencies the effective minimum supported rust version may increase as a result of the activities out of control of exonum developers the decision how to deal with this situation pin the dependency or bump the minimum supported rust version will be made on the case by case basis contributing to contribute to exonum please see contributing contributing md see also some exonum stuff that is not in this repository java language support https github com exonum exonum java binding javascript light client https github com exonum exonum client python light client https github com exonum exonum python client high level documentation https github com exonum exonum doc dynamic service launcher for exonum https github com exonum exonum launcher | blockchain rust cryptography bitcoin p2p consensus-algorithm byzantine database | blockchain |
ProjectMobile | projectmobile mobile development | front_end |
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Stories.JSON | stories json stories json images stories json logo png stories json is an open source format for formatgpt http formatgpt com for composing large language models such as gpt chatgpt codex stable diffusion etc into beautiful documents created by kevin ashley labs http kevinashley com and used by ask ai https askainow com livebook ai https livebookai com and many other applications for prompt composition example stories json src examples stories json formatgpt api and python library the python libary and api for processing stories json is available for commercial licensing please contact kevin ashley https www linkedin com in kashlik http formatgpt com images formatgpt png http formatgpt com showcases stories json is used in the following projects askai images logo long color 40 png ask ai https askainow com ai training and learning platform livebook ai images livebook 40 png livebook ai https livebookai com category ai stories platform ai kiosk images ai kiosk png ai kiosk https livebookai com post kiosk at the computer history museum interview with artificial intelligence https www amazon com dp b0bnv3mn53 book uses stories json for code examples interview with artificial intelligence images book cover 200 png guide to stories json stories json is a template format used by stories json composition engine also known as formatgpt stories json templates are scripts for story structures enabling large language models llms to compose large texts for example articles books novels etc from aristotle with his concept of beginning middle and end or 3 acts structure to various structures used today to write blogs technical articles etc stories json allows you to create flexible structures for your content and avoid large scale models limitations on size of produced text and limited inference memory which for gpt 3 was 2048 tokens without a composition engine like stories json formatgpt large scale models can only produce short text fragments usually without a strong logical connection formatgpt also makes possible formatting and writing rich documents that include elements outside of nlp such as images etc let s look at a template for a professional article src examples stories json and prompt definitions src examples professional article json professional article args template name professional article display name business and professional category category category filter business and professional number 6 usage chars 0 parts similarity 0 0 min length blocks 7 prompt plot args category number run once prompt title args category plot format h 1 run once limit 80 type title prompt concise summary args category title plot run once prompt introduction args category title concise summary run once format paragraph section facts repeat number steps prompt fact title args plot index format h 3 type title prompt fact args category fact title plot index format paragraph prompt conclusion args category title plot run once format paragraph as you can see the template contains several sections stories json is dictionary containing multiple templates uniquely identified by name getting started with stories json aristotle s three acts structure can be easily modeled with stories json as well as many other composition types to write great story structures you can start with pre built templates in formatgpt directory interesting facts is great for popular stories professional article writes multi title story structure live is used for fiction 3 acts structure poetry is a poetry specific template recipe can write recipes interview problem template provides you can create templates that generate text images code and richly formatted text output for example problems created for my book interview with artificial intelligence https www amazon com dp b0bnv3mn53 are defined in stories json interview problem src examples stories json template variables there re two types of variables in templates the ones marked by are used for prompt substitutions prompts are used to provide inputs to the models for example a variable title is used to define a title it is also likely to be used in prompts for example text write an introduction for the article titled title in category category summarized below the introduction has to be long enough at least several paragraphs and compelling to grab readers attention and pull them in summary concise summary long introduction prompts in the prompt example above there re several substitute variables title category concise summary prompts are placed inside the template folder and are simple text files with substitute variables text autowriter template folder models plot json model parameters title json plot txt prompts title txt an example of a parameterized plot notice substitute variables category title and concise summary in the prompt write an introduction for the article titled title in category category summarized below the introduction has to be long enough at least several paragraphs and compelling to grab readers attention and pull them in summary concise summary long introduction template arguments each template contains an args section that includes input parameters as the formatgpt engine runs the template it adds new values to the args section json args template name professional article display name business and professional category category category filter business and professional number 6 usage chars 0 parts similarity 0 0 min length blocks 7 special parameters there re several parameters that are very useful for any text generation so formatgpt will always produce the following title the title of the story as it appears on livebookai category this is important for categorization of the stories using standard format for book categories e g fiction romance historical or business technology artificial intelligence plot plot is often hidden from the site but is always used as a concise outline of the whole story concise summary this is an abstract of your story it s important that initially you may not have those variables but as the template runs formatgpt will fill them for you if you don t specify them in the input template execution template execution begins with args section which sets input parameters and executes steps sequentially loops and repetitions are allowed templates are persisted with each step providing a restart capability for steps at any point for example look at the first step after args in the template above json prompt plot args category number run once the step starts with prompt parameter which defines the prompt the name of the prompt is plot prompt plot notice that we don t have plot in args section so formatgpt loads prompt file named plot txt from the template directory makes known parameters substitutions args category number from the args section of the template and runs the model model parameters are stored as json files in models folder in the template folder so formatgpt will use models plot json as parameters file for the model call there s also a parameter run once which means that this step will only run once and will not rerun as formatgpt picks the template this is important to avoid repetitions when template is scheduled prompt step prompts are core to ai inference formatgpt s prompt step includes rich methods that substitute prompt parameters at run time store variables and prevent running over the budget of tokens if you look at the plot txt file for the plot you ll see that it uses both arguments defined in template steps category and number and the plot will produce 6 topics number 6 in the args section above text for an article in category category think of a subject then write number outline topics including a topic for a problem statement and a solution topics now comes the exciting part formatgpt calls ai model in this step but where s the result the result is saved in the plot variable notice the sign and added to args formatgpt also copies it into prev variable which is helpful in the next steps model output the model output for each step is saved in the variable with the name of the prompt so prompt named plot will store the output in plot variable in args section of the template in addition formatgpt also copies the last prompt output to prev variable this becomes very useful in the next steps to refer to results of previous steps our template initially had two variables defined in args section category and number now after formatgpt call to ai model we have plot result example generate title now we have category plot variables defined i e in the previous steps formatgpt filled their values either from input arguments or by calling ai model plot so formatgpt moves to the next step to generate title our input parameters for this step are args category plot category was defined in the input args but plot was just freshly generated by ai model and we re using it to make a title json prompt title args category plot format h 1 run once limit 80 type title notice that title step has a few new parameters format h 1 this tells formatgpt that the result of this step should be added to the output text of our story and formatted as a header of the first level limit 80 tells formatgpt to limit the title to 80 characters note if you want to limit the size of the prompt output use an appropriate max tokens value in the model json this prevents ai model from producing large output limit will only limit the output result by word boundaries json engine text davinci 002 temperature 0 7 max tokens 18 top p 1 0 frequency penalty 0 presence penalty 0 stop sequence topics concise title in the above example title json max tokens is set to 18 or roughly 72 characters still models can return more characters to limit puts a hard limit to that formatted output formatgpt was designed to produce richly formatted output including titles lists images code and more to tell formatgpt that you want the step to format the output include format in the step valid values are h n header n level i e 1 6 paragraph format output as paragraph code format output as code recipe especially formatted recipe loops and repetitions what if we need to call the same step multiple times while formatgpt is not recursive it allows loop sections json section facts repeat number steps prompt fact title args plot index format h 3 type title prompt fact args category fact title plot index format paragraph in this section called facts we make use of repeat number variable remember that our number 6 and we used it earlier to create topics for our plot this section has 2 sub steps fact title and fact so formatgpt executes these steps and repeats the section until it exceeds number times loops are very useful if you want to generate long text scheduler livebookai can schedule templates that typically involves running a subset of template steps usually title plot and concise summary when the template is scheduled you have an advantage of seeing the future publication including title plot and abstract the partially executed template json is stored in the database and later retrieved when the template completes content step producing formatted content in addition to format tag in prompt steps there s also a dedicated content step that produces a formatted output the following content step produces a custom title json content nutrition facts format h 5 please see format specification above for valid values inserting delimiters in output a special case for a content tag is a delimiter it s frequently used in poetry or books to separate sections json content format delimiter set step assigning variable values the set step allows you to set a variable to another variable json set prev value rising action this may be useful to copy values lastlines suffix limiting text size to the last few lines one of the problems with llms is the limited size of the prompt to limit the size of the variable sebt to the prompt use lastlines variable suffix e g prev last lines json prompt summary args category title summary prev last lines inline variable assignment occasionally you need to set the variable inline without a separate set step to do so use operator e g summary prev will set summary variable to the current value of prev json prompt summary args category title summary prev prev last lines formatgpt python library and api for formatgpt and stories json is available for commercial licensing how to use as a standalone python library include in your code as a docker container service use as container moderate development as a multi tenant api service get api key and access minimum development please contact kevin ashley https www linkedin com in kashlik for details of integration into your project http formatgpt com images formatgpt 1 jpg http formatgpt com | ai |
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ml-system-design-pattern | japanese readme ja md korean readme ko md machine learning system design pattern this repository contains system design patterns for training serving and operation of machine learning systems in production objectives the main objective of this document is to explain system patterns for designing machine learning system in production br this document is not the design patterns for developing machine learning model to achieve certain performance in accuracy though some columns may refer to those use cases br prerequisites all of the ml system patterns are designed to be deployed on a public cloud or a kubernetes cluster the document tries not to be dependent on a certain programming language or platform as possible though since python is the most major language for the machine learning technology most of the patterns can be developed with python br for reading please refer below for reading br github pages https mercari github io ml system design pattern sample implementations some sample implementations are available below https github com shibuiwilliam ml system in actions patterns serving patterns serving patterns readme md the serving patterns are a series of system designs for using machine learning models in production workflow web single pattern serving patterns web single pattern design en md synchronous pattern serving patterns synchronous pattern design en md asynchronous pattern serving patterns asynchronous pattern design en md batch pattern serving patterns batch pattern design en md prep pred pattern serving patterns prep pred pattern design en md microservice vertical pattern serving patterns microservice vertical pattern design en md microservice horizontal pattern serving patterns microservice horizontal pattern design en md prediction cache pattern serving patterns prediction cache pattern design en md data cache pattern serving patterns data cache pattern design en md prediction circuit break pattern serving patterns prediction circuit break pattern design en md multiple stage prediction pattern serving patterns multiple stage prediction pattern design en md serving template pattern serving patterns serving template pattern design en md edge prediction pattern to do antipatterns serving patterns anti patterns readme md online bigsize pattern serving patterns anti patterns online bigsize pattern design en md all in one pattern serving patterns anti patterns all in one pattern design en md qa patterns qa patterns readme md pattens to evaluate model as well as prediction server shadow ab testing pattern qa patterns shadow ab test pattern design en md online ab testing pattern qa patterns online ab test pattern design en md loading test pattern qa patterns loading test pattern design en md antipatterns qa patterns anti patterns readme md offline only pattern qa patterns anti patterns offline only pattern design en md training patterns training patterns readme md patterns to construct training pipeline batch training pattern training patterns batch training pattern design en md pipeline training pattern training patterns pipeline training pattern design en md parameter and architecture search pattern training patterns parameter and architecture search pattern design en md antipatterns training patterns anti patterns readme md only me pattern training patterns anti patterns only me pattern design en md training code in serving pattern training patterns anti patterns training code in serving pattern design en md too many pipes pattern training patterns anti patterns too many pipes pattern design en md operation patterns operation patterns readme md the operation patterns contain configuration logging monitoring and alerting system designs for machine learning system model in image pattern operation patterns model in image pattern design en md model load pattern operation patterns model load pattern design en md data model versioning pattern operation patterns data model versioning pattern design en md prediction log pattern operation patterns prediction log pattern design en md prediction monitoring pattern operation patterns prediction monitoring pattern design en md parameter based serving pattern operation patterns parameter based serving pattern design en md condition based serving pattern operation patterns condition based serving pattern design en md antipatterns operation patterns anti patterns readme md no logging pattern operation patterns anti patterns no logging pattern design en md nobody knows pattern operation patterns anti patterns nobody knows pattern design en md lifecycle patterns lifecycle patterns readme md the lifecycle patterns contain composition of several patterns to realize actual ml system with operation train then serve pattern lifecycle patterns train then serve pattern design en md training to serving pattern lifecycle patterns training to serving pattern design en md antipatterns lifecycle patterns anti patterns readme md todo committers yusuke shibui shibuiwilliam https github com shibuiwilliam sung yun byeon zzsza https github com zzsza jiyeon seo jiyeonseo https github com jiyeonseo daeyoon jin zetbouaka https github com zetbouaka contribution for adding a new pattern please use template design md template design md as a template and raise an issue and later pr br for adding a new antipattern please use template antipattern md template antipattern md as a template and raise an issue and later pr br to request for improvement change or question please propose an issue br please read the cla carefully before submitting your contribution to mercari under any circumstances by submitting your contribution you are deemed to accept and agree to be bound by the terms and conditions of the cla https www mercari com cla license copyright 2020 mercari inc licensed under the mit license license | ai |
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ROS-Turtlebot2 | ros project repository for robotics project module on ros platform and turtlebot 2 implementing a set of computer vision and autonomous navigation algorithms using tutlebot 2 with ros hydro lidar is used for mapping and amcl approach to localize the robot in a map microsoft kinect is used for capturing visual and depth information computer vision tasks such as ar tag detection and localization face detection and recognition has been realized alongside using smach framework for concurrency initial pose estimation of the robot was also done using visual information obtained from ar tags python was used as the main programming language with a set of open source packages such as opencv rplidar rbx1 and rbx2 demo showcase https youtu be 3vn96tyyq k license licensed under the mit license http www opensource org licenses mit | ai |
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MobileConsoleKit | mobile console kit mobile console kit is a unity asset store https assetstore unity com packages tools gui mobile console kit 128118 that help you not just viewing logs in mobile devices but helping you monitor do experiment and speed up development time mobile console kit depends on textmesh pro for high performance and customization you need to install textmesh pro and import essential resources before installing mobile console kit install via upm https github com pixeption mobileconsolekit git key features enable disable mobile console with a single click no trace left highly optimization can handle dozen of thousands of logs supports basic functions like filter log by type collapse supports log channel search log with regex share log via native share unified portrait and landscape ui resizable window and adjustable background transparency user defined setting and commands allow you to create your own tools comes with powerful tools including application device info search gameobject by name tag component inspect playerprefs inspect persistent data table of content for more detail on each features please see the wiki below basic enable disable mobile console https github com pixeption mobileconsolekit wiki enable console tool overview https github com pixeption mobileconsolekit wiki log channel https github com pixeption mobileconsolekit wiki log channel share log https github com pixeption mobileconsolekit wiki share log setting executable commands https github com pixeption mobileconsolekit wiki commands built in commands search game object playerprefs inspector persistent data inspector https github com pixeption mobileconsolekit wiki built in commands advance built in log https github com pixeption mobileconsolekit wiki built in log console settings https github com pixeption mobileconsolekit wiki console settings view builder https github com pixeption mobileconsolekit wiki view builder migrate from v1 0 to v2 0 as there are many parts of the tool have been rewritten it s recommended that you need to delete the whole mobile console folder before import the new one video demo v1 0 introduction https youtu be igduixixl1q v2 0 showcase https youtu be aqfkz8 pjqu demo apk playstore https play google com store apps details id com pixeption mck showcase github https github com pixeption mobileconsolekit releases download demo v2 0 demo apk preview images tool overview https i imgur com 8to6mb1 png https i imgur com mrw3zcq png https i imgur com ucqadl7 png share log view https i imgur com lq0weze png setting view https i imgur com rc0tghi png command view https i imgur com y8flnun png log channel filter https i imgur com llox3y6 png app and device info https i imgur com tmz2uvn png persistent data inspector https i imgur com z3jf5bt png https i imgur com risjjgh png playerprefs inspector https i imgur com dp0e2rx png search gameobject https i imgur com qpqgmmw png https i imgur com 3ueq451 png | front_end |
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Traffic-Light-Controller-using-Verilog | traffic light controller using verilog youtube video link https www youtube com watch v yt7no6rwcvk the project includes system design of a t intersection traffic light controller and its verilog code in vivado design suite | verilog verilog-hdl verilog-project verilog-code verilog-programs verilog-simulator vivado vivado-hls vivado-simulator traffic traffic-light traffic-sign-recognition | os |
FoBo | fobo a modular front end toolkit module for lift stories in ready https badge waffle io karma4u101 fobo svg label ready title ready http waffle io karma4u101 fobo build status https secure travis ci org karma4u101 fobo png http travis ci org karma4u101 fobo gitter https badges gitter im join 20chat svg https gitter im karma4u101 fobo utm source badge utm medium badge utm campaign pr badge utm content badge fobo is a lift module http liftweb net lift modules composed of several toolkit modules that includes industry leading open source front end toolkits for developing mobile scalable responsive web applications that will jazz up your lift applications with the toolkit s of your choice the toolkit modules is in it s turn composed of a api module and a resource module artifact making the usage of fobo scalable and flexible a live demo and introduction to using the fobo module s including unified scaladoc api documentation https www media4u101 se fobo lift template demo foboapi current net liftmodules fobo index html and links to running examples of starter templates and more can be seen at the fobo template demo http www media4u101 se fobo lift template demo using the lift module you will get a hassle free inclusion and smooth upgrades of toolkits you decide what toolkits artifacts and versions to enable the fobo module is making development maintenance upgrade and fall back quick and easy typically a one liner change in lift boot by simultaneously supporting several versions of the included toolkits and without code or reference changes providing debug able js and css files in development and minimized files otherwise using this module you will also get a clean separation of the toolkit files and your application specific resources as the module dose not clutter your applications web app resources directory fobo api some of the fobo toolkit modules also includes a evolving fobo lift api module that includes snippet s and helper object s see api documents for usage that will take care of some common toolkit and component initiation and usage like lift site map to bootstrap or angular material menu builders breadcrumbs resource injection script generation and more fobo lift starter template bootstrap v4 a lift v3 x starter template with fobo setup using bootstrap v4 x and fobo s bootstrapmegametaprotouser for mapper protouser views is available from here fobo demo https github com karma4u101 fobo demo material design a lift v3 x starter template with fobo setup using angular material and fobo s materialmegametaprotouser for mapper protouser views is available from here fobo demo https github com karma4u101 fobo demo bootstrap v3 a lift v3 x starter template with fobo setup using bootstrap v3 x and fobo s bootstrapmegametaprotouser for mapper protouser views is available from here lift v3 x template applications https github com lift lift 30 sbt bootstrap v3 a lift v2 6 starter template with fobo setup using bootstrap v3 x and fobo s bootstrapmegametaprotouser for mapper protouser views is available from here lift v2 6 template applications https github com lift lift 26 sbt for more information on how to get started with fobo s starter templates see quick start with lift template applications quick start with lift template applications if you have ideas and suggestions let us know contributions improvements contributions and suggestions are welcome pick a issue marked as ready by the waffle badge above or open a new issue and start working on a pr please see the contribution document https github com karma4u101 fobo blob master contributing md you can also leave a issue report or drop a question suggestion to the lift s mailing list http groups google com group liftweb as fobo is using this git branching model http nvie com posts a successful git branching model the main branch is just updated for releases so your pull requests will by default be against the development branch https github com karma4u101 fobo blob develop integration into your project dependency settings general this section describes the general naming pattern for using any of fobo s modules sbt add this to your project build sbt files librarydependencies section scala net liftmodules modulename x1 y1 x2 y2 z2 snapshot rcx mx maven add this in the dependency section of your pom file xml dependency groupid net liftmodules groupid artifactid modulename x1 y1 a b c artifactid version x2 y2 z2 snapshot rcx mx version dependency where x1 y1 is lift major and minor version numbers and a b c is scala version number and x2 y2 z2 is the module s major x2 minor y2 and eventual incremental numbers z2 followed by a eventual snapshot release candidate rcx or milestone mx version part dependency settings fobo s fobo module to get access to all fobo s toolkit resource and api modules you can use fobo s fobo module as shown bellow this is the simplest and probably most common way to use fobo setup example sbt scala net liftmodules fobo 3 3 2 1 0 maven xml dependency groupid net liftmodules groupid artifactid fobo 3 3 2 12 6 artifactid version 2 1 0 version dependency the example shows how to include the fobo fobo module built for lift 3 3 x if you are using maven observe that the artifact id also needs the scala version dependency setting toolkit api resource to get access to exactly the fobo toolkit resource and or api module s you expect to use in your project you can use something like the following for more information see respective toolkit api or resource modules readme setup example using the fobo lift bootstrap3 api module sbt scala net liftmodules fobo twbs bootstrap4 api 3 3 2 1 0 maven xml dependency groupid net liftmodules groupid artifactid fobo twbs bootstrap4 api 3 3 2 12 6 artifactid version 2 1 0 version dependency the example shows how to include the fobo bootstrap4 api module built for lift 3 2 x if you are using maven observe that the artifact id also needs the scala version lift fobo boot hooks alternative using the fobo fobo module to get access to all fobo s toolkit resource and api modules use the following into your lift boot scala import net liftmodules fobo initiate a toolkit to initiate usage of both resource and api fobo toolkit init fobo toolkit toolkitobjectxyz fobo toolkit init fobo toolkit additional toolkit object name you can also initate one or more resources modules excluding ev lift fobo api fobo resource init fobo resource resouceobjectxyz fobo resource init fobo resource additional resource object name you can also initiate one or more api modules providing the corresponding resource yourself fobo api init fobo api apiobjectxyz fobo api init fobo api additional api object name alternative using one of fobo s toolkit resource and or api modules scala import net liftmodules foboxy fobo as above if toolkit fobo toolkit init fobo toolkit toolkitobjectxyz as above if resource fobo resource init fobo resource resouceobjectxyz as above if api fobo api init fobo api apiobjectxyz alternative using several independently added fobo modules but not all scala import net liftmodules foboxy1 foboxy2 toolkits foboxy1 toolkit init foboxy1 toolkit toolkitobjectxyz foboxy2 toolkit init foboxy2 toolkit toolkitobjectxyz resource foboxy1 resource init foboxy1 resource resouceobjectxyz foboxy2 resource init foboxy2 resource resouceobjectxyz api foboxy1 api init foboxy1 api apiobjectxyz foboxy2 api init foboxy2 api apiobjectxyz lift fobo template hooks put something like the following in your lift template s head section see below for available names html link rel stylesheet type text css href classpath fobo css file name link rel stylesheet type text css href classpath fobo tolkit name css file name link rel stylesheet type text css href classpath fobo tolkit name another css file name link rel stylesheet type text css href classpath fobo a third css file name link rel stylesheet type text css href path to you app specific css file in the webapp dir script type text javascript src classpath fobo script file name script script type text javascript src classpath fobo tolkit name script file name script script type text javascript src classpath fobo tolkit name another script file name script script type text javascript src classpath fobo a third script file name script for more information see readme in respective toolkit module you can now also use use fobo s resource injection snippet to inject all your fobo css and js resources html link data lift fobo resources injectcss resources file name another file name a third file name link link rel stylesheet type text css href path to you app specific css file in the webapp dir script data lift fobo resources injectjs resources file name another file name a third file name script for more information see fobo api resources http www media4u101 se fobo lift template demo foboapi index html net liftmodules fobo snippet fobo resources module names the following is a list of names that can be used in lift boot either pulled in by the fobo meta module or as stand alone sub modules for more information se respective sub modules readme angularjsxyz with components ajsuibootstrapxyz ajsuigridxyz ajsnggridxyz ajmaterialxyz bootstrapxyz fontawesomexyz highlightjsxyz jqueryxyz jquerymigratexyz pacexyz prettifyxyz popper where x is major y minor and z incremental version numbers as seen in the toolkits list above for example bootstrapxyz will be bootstrap230 for twitter bootstrap v2 3 0 for more information on how to set this up see below fobo modules toolkit s and available versions fobo artifacts is available for lift 2 4 2 5 2 6 x 3 0 x 3 1 x 3 2 x and 3 3 x fobo https github com karma4u101 fobo tree master fobo fobo this module contains all the bellow listed toolkit modules fobo is built up of several toolkit that in it s turn is built up of corresponding api and resource modules apart from being accessible from the fobo fobo module the toolkit api and resource modules is also available for use separately the fobo fobo module includes the following modules and supports the following toolkit versions fobo angularjs https github com karma4u101 fobo tree master angular v1 0 6 v1 2 11 v1 3 15 v1 4 1 v1 4 8 v1 5 3 and components see fobo angularjs https github com karma4u101 fobo tree master angular angularjs angular http angularjs org fobo twbs bootstrap4 https github com karma4u101 fobo tree master bootstrap bootstrap4 v4 0 0 v4 1 3 bootstrap 4 x https getbootstrap com docs 4 1 fobo twbs bootstrap3 https github com karma4u101 fobo tree master bootstrap bootstrap3 v3 0 0 v3 0 1 v3 1 1 v3 2 0 v3 3 5 v3 3 6 v3 3 7 bootstrap 3 x http getbootstrap com fobo twitter bootstrap https github com karma4u101 fobo tree master bootstrap bootstrap2 v2 3 2 twitter bootstrap http twitter github com bootstrap fobo font awesome https github com karma4u101 fobo tree master font awesome v2 0 0 v3 0 0 v3 2 1 v4 0 1 v4 0 3 v4 1 0 v4 3 0 v4 5 0 v5 5 0 font awesome http fortawesome github com font awesome fobo highlight https github com karma4u101 fobo tree master highlight v9 3 0 highlight js https highlightjs org fobo jquery https github com karma4u101 fobo tree master jquery v1 7 2 v1 8 2 v1 9 1 v1 10 2 v1 11 0 v1 11 1 v1 11 3 v2 1 1 v2 1 4 v2 2 4 v3 0 0 v3 1 0 jquery http jquery com fobo jquery also includes jquery migrate v1 2 1 v1 4 1 v3 0 0 jquery migrate https github com jquery jquery migrate fobo google code prettify https github com karma4u101 fobo tree master google code prettify vjun2011 google code prettify https github com google code prettify fobo pace https github com karma4u101 fobo tree master pace v0 4 15 v1 0 2 pace http github hubspot com pace docs welcome popper https github com karma4u101 fobo tree master popper v1 10 8 popper https popper js org note some of the listed toolkit versions may have been deprecated and possible removed after having been deprecated in 2 releases available css and javascript files see the lift fobo template hooks section in readme file for respective fobo toolkit module listed above quick start with lift template applications the only prerequisites for using this lift module is that you have git and java installed and configured on the target computer but a suitable lift template project will also come in handy a scala v2 11 lift v2 6 x starter template with fobo setup using bootstrap v3 x and fobo s bootstrapmegametaprotouser for mapper protouser views is available from here lift v2 6 template applications https github com lift lift 26 sbt a scala v2 12 lift v3 0 starter template with fobo setup using bootstrap v3 x and fobo s bootstrapmegametaprotouser for mapper protouser views is available from here lift v3 0 template applications https github com lift lift 30 sbt you don t need to use it but this project s also includes a eclipse and a idea plug in for browsing the code see the scala ide section below scala ide for eclipse sbteclipse provides sbt command to create eclipse project files 1 usage project sbt compile eclipse 2 in eclipse do file import select general existing project into workspace use brows to look up the project root note the compile step prior to the eclipse command in 1 is needed for eclipse to recognize the generated buildinfo scala file that is part of fobo s build system updates for a comprehensive list of updates see changelog https github com karma4u101 fobo blob develop changelog md old submodules modules available as maven artifacts but that have been split out of the fobo fobo module the modules can still be used as stand alone modules lift version 3 0 sonatype fobo jquery mobile https oss sonatype org nexus search quick fobo jquery mobile v1 0 1 v1 1 0 jquery mobile http jquerymobile com usage fobojqm initparam toolkit fobojqm jquerymobile110 sonatype fobo datatables https oss sonatype org nexus search quick fobo datatables v1 9 0 datatables net http datatables net usage fobodt initparam toolkit fobodt datatables190 sonatype fobo knockout https oss sonatype org nexus search quick fobo knockout v2 0 0 v2 1 0 v2 2 1 knockoutjs http knockoutjs com usage foboko initparam toolkit foboko knockout221 sonatype fobo foundation https oss sonatype org nexus search quick fobo foundation v2 1 5 zurb foundation http foundation zurb com usage fobofo initparam toolkit fobofo foundation215 sonatype fobo kinetic oss sonatype org nexus search quick fobo kinetic v5 1 0 kinetic http kineticjs com usage fobofo initparam toolkit fobofo kinetic510 for feature listings on all releases see changelog https github com karma4u101 fobo blob develop changelog md contributors the fobo lift module was created by peter petersson http www media4u101 se peter petersson gmail com twitter karma4u101 https twitter com karma4u101 contributors byrongibson lawliets the lift module conventions were developed by the lift community http groups google com group liftweb | scala lift javascript-library css-framework | front_end |
Aquila2 | p align left a href readme cn md a nbsp nbspenglish nbsp p br br p align center img src assets logo png width 400 p br p align center a href https huggingface co baai hugging face a nbsp nbsp nbsp nbsp a href https model baai ac cn models img src assets baai png width 18 baai modelhub a nbsp nbsp nbsp nbsp a href assets wechat qrcode jpg wechat a p br we announce that our aquila2 series is now open source comprising aquila2 the base language models aquila2 7b and aquila2 34b and aquilachat2 the chat models namely aquilachat2 7b and aquilachat2 34b as well as the long text chat models namely aquilachat2 7b 16k and aquilachat2 34b 16k you can find the links in the following table kindly click on them to access the model cards model name download sources aquila2 7b img src assets baai png width 18 https model baai ac cn model detail 100118 https huggingface co baai aquila2 7b aquilachat2 7b img src assets baai png width 18 https model baai ac cn model detail 100117 https huggingface co baai aquilachat2 7b aquilachat2 7b 16k img src assets baai png width 18 https model baai ac cn model detail 100120 https huggingface co baai aquilachat2 7b 16k aquila2 34b img src assets baai png width 18 https model baai ac cn model detail 100119 https huggingface co baai aquilachat2 34b aquilachat2 34b img src assets baai png width 18 https model baai ac cn model detail 100116 https huggingface co baai aquilachat2 34b aquilachat2 34b 16k img src assets baai png width 18 https model baai ac cn model detail 100121 https huggingface co baai aquilachat2 34b 16k in this repo you can figure out quickstart with aquila2 tutorials on finetuning including full parameter lora and q lora long context understanding and evaluation license agreement please don t hesitate to bring up issues and feel free to submit pull requests prs at any time p s better in english for wider comprehension we re always enthusiastic about contributions br br news and updates 2023 10 12 we release aquila2 series on baai modelhub and hugging face br br performance aquila2 series outperform the models of similar model sizes on a series of benchmark datasets base model performance br p align center img src assets base metrics jpeg width 1024 p br p in evaluating generative chat models our team prioritizes how models autonomously respond to questions a reflection of real world user interactions guided by stanford university s helm 1 approach our assessment emphasizes context understanding and instruction adherence in some cases models may deliver answers not in line with the instruction of input resulting in a 0 score for instance if the model should respond with a but outputs b or the answer is a it earns a 0 other industry methods include concatenating question answer and assessing the combined text s probability however in this method the chat model doesn t generate content but computing probability scores due to its divergence from real world chat scenarios we haven t adopted this approach in our evaluations br 1 https crfm stanford edu helm latest p br long context performance br model method avg en avg zh avg vcsum zh br chinese lsht zh br chinese hotpotqa br english 2wikimqa br english gpt 3 5 turbo 16k 33 6 44 7 22 6 16 0 29 2 51 6 37 7 aquilachat2 34b 16k pi sft 31 7 40 2 23 3 16 5 30 0 41 9 38 5 chatglm2 6b 32k pi sft 30 8 39 6 22 0 16 2 27 7 45 1 34 0 aquilachat2 7b 16k pi sft 29 5 31 7 27 2 14 4 40 0 36 1 27 3 internlm 7b 8k 22 4 30 6 14 3 13 0 15 5 33 3 27 9 chatglm2 6b none 22 1 26 6 17 6 14 6 20 5 33 0 20 2 longchat 7b v1 5 32k pi sft 21 7 26 1 17 4 14 0 20 8 31 5 20 6 baichuan2 7b chat none 21 3 25 9 16 8 13 6 20 0 32 8 18 9 internlm 20b chat none 16 6 24 3 8 9 11 9 6 0 24 4 24 2 qwen 14b chat dynamic ntk 16 1 20 8 11 5 16 6 6 4 22 9 18 8 xgen 7b 8k pre train 16 0 21 3 10 8 1 5 20 0 14 2 28 3 llama2 7b chat 4k none 14 0 18 0 10 0 0 2 19 8 11 6 24 3 baichuan2 13b chat none 10 5 14 8 6 3 7 0 5 5 16 0 13 6 br reasoning tasks performance br model avg babi 16 br inductive clutrr br inductive babi 15 br deductive entailmentbank br deductive nli br abductive e care br casual baichuan2 7b chat 47 8 40 0 26 7 43 3 73 3 53 3 50 0 qwen 7b chat 49 5 20 0 10 0 66 7 86 7 56 7 56 7 qwen 14b chat 51 1 26 7 10 0 63 3 86 7 63 3 56 7 baichuan2 13b chat 53 3 33 3 10 0 66 7 80 0 66 7 63 3 internlm 20b chat 53 9 46 7 13 3 43 3 80 0 70 0 70 0 chatgpt 55 6 46 7 6 7 86 7 83 3 63 3 46 7 llama 70b chat 57 2 63 3 20 0 53 3 80 0 66 7 60 0 gpt 4 81 1 93 3 36 7 100 0 90 0 83 3 83 3 aquilachat2 34b 58 3 43 3 16 7 63 6 80 0 80 0 66 7 aquilachat2 34b sft 65 6 73 3 16 7 76 7 80 0 76 7 70 0 aquilachat2 34b sft cot 69 4 80 0 23 3 83 3 73 3 80 0 76 7 br requirements python 3 10 and above pytorch 1 12 and above 2 0 and above are recommended transformers 4 32 and above cuda 11 4 and above are recommended this is for gpu users flash attention users etc br br quickstart we have provided a straightforward example to illustrate how to quickly get started with aquila2 before proceeding ensure that your environment is properly configured and that the necessary packages have been installed first and foremost ensure that these prerequisites are met and then follow the instructions below to install the necessary libraries and dependencies pip install r requirements txt if your device supports fp16 or bf16 precision we also recommend installing flash attention to enhance execution speed and reduce memory consumption it s important to note that flash attention is optional and the project can be executed normally without it for the installation of flash attention please follow the instructions in https github com dao ailab flash attention using docker tar file for the environment that meets these requirements https docs nvidia com deeplearning frameworks pytorch release notes rel 23 06 html you can also set up the environment required for aquila2 by directly downloading the docker tar file https model baai ac cn model detail 220118 then loading and running it because of all already installed dependencies in the container you just pull all sources flagai https github com flagai open flagai git and aquila2 https github com flagai open aquila2 git and include both paths int environment variable like export pythonpath flagai home aquila2 home pythonpath now you can use img src assets baai png width 18 baai modelhub or transformers to run our model img src assets baai png width 20 modelhub you can now use the aquilachat2 7b model for inference as follows python from flagai auto model auto loader import autoloader model name model name aquilachat2 7b model name aquilachat2 34b load the model and tokenizer autoloader autoloader aquila2 model name model name to modify the model loading path use the model dir parameter autoloader autoloader aquila2 model dir checkpoints model name model name to load the lora module you need to provide the path to the lora module autoloader autoloader aquila2 model name model name lora dir examples checkpoints lora aquila2chat to load the lora module you need to provide the path to the lora module autoloader autoloader aquila2 model name model name qlora dir examples checkpoints qlora aquila2chat model autoloader get model tokenizer autoloader get tokenizer example test data write a tongue twister that s extremely difficult to pronounce for text in test data print model predict text tokenizer tokenizer model name model name for aquila2 7b or aquila2 34b you need to set sft false print model predict text tokenizer tokenizer model name model name sft false the results of our execution are as follows harry had a harpy flight fred had a fiddle and george had a gecko for breakfast say that three times fast and see how long you can make it last br br transformers python from transformers import autotokenizer automodelforcausallm import torch device torch device cuda 0 model info baai aquilachat2 7b tokenizer autotokenizer from pretrained model info trust remote code true model automodelforcausallm from pretrained model info trust remote code true torch dtype torch bfloat16 model eval model to device text 10 from predict import predict out predict model text tokenizer tokenizer max gen len 200 top p 0 95 seed 1234 topk 100 temperature 0 9 sft true device device model name aquilachat2 7b print out quantization before using quantization bitsandbytes needs to be installed pip install bitsandbytes after that you re all set to use the quantized models for inference usage python import torch from flagai auto model auto loader import autoloader from transformers import bitsandbytesconfig model name aquilachat2 7b autoloader autoloader aquila2 model name model name quantization config bitsandbytesconfig load in 4bit true bnb 4bit use double quant true bnb 4bit quant type nf4 bnb 4bit compute dtype torch bfloat16 model autoloader get model tokenizer autoloader get tokenizer test data write a tongue twister that s extremely difficult to pronounce for text in test data print model predict text tokenizer tokenizer model name model name aquilachat2 34b 4bit version has 99 3 of the performance of the bf16 version the 4bit version of aquilachat2 34b offers significantly better performance than the 7b model and has similar memory usage img src assets table png align center pretraining from aquila2 we upgrade the underlying pretraining framework which is now open sourced as flagscale https github com flagopen flagscale it is based on the megatron lm project and aims at utilizing the computation resources efficiently for llms without sacrificing the numerical stability and model effectiveness in flagscale we firstly provide our actually used training schemes for aquila2 7b and aquila2 34b including the parallel strategies optimizations and hyper parameter settings by using flagscale our model flops utilization can achieve a very high level for both aquila2 7b and aquila2 34b for now flagscale is still in its early stage and we will work with the community together to support different llms on various hardware architectures in the future finetuning usage we provide users with a series of fine tuning scripts designed to adapt models to various downstream tasks using custom data within the comments section of the scripts users will find detailed instructions indicating which parameters may need adjustments based on specific needs before initiating the fine tuning process you are required to have your training data prepared all samples should be consolidated into a list and stored in a json file each sample should be represented as a dictionary encompassing an id and conversation with the latter presented in list format below is an example for your reference json id alpaca data json 1 conversations from human value what are the three primary colors from gpt value the three primary colors are red blue and yellow instruction subsequently you can use the variety of fine tuning scripts we offer for different purposes execute finetune 7b finetune sh for a full parameter fine tuning of the 7b model execute finetune 7b finetune lora sh for lora fine tuning of the 7b model execute finetune 7b finetune qlora sh for q lora fine tuning of the 7b model execute finetune 34b finetune sh for a full parameter fine tuning of the 34b model execute finetune 34b finetune lora sh for lora fine tuning of the 34b model execute finetune 34b finetune qlora sh for q lora fine tuning of the 34b model note that you are required to specify the path to the training data within the script and configure the hostfile accordingly if a custom model file is not provided in the script it will automatically download the corresponding model from modelhub based on the specified model name and proceed with the fine tuning operation to perform full parameter fine tuning execute the following scripts bash fine tuning the 7b model bash finetune 7b finetune sh fine tuning the 34b model bash finetune 34b finetune sh the fine tuning approach of lora as detailed in the paper https arxiv org abs 2106 09685 varies from the full parameter method lora solely updates the parameters of the adapter layer without modifying the original language model parameters this practice reduces memory and computational overhead applicable to a variety of model sizes and tasks lora facilitates more efficient model fine tuning to cater to specific tasks or datasets to implement lora execute the following scripts bash fine tuning the 7b model bash finetune 7b finetune lora sh fine tuning the 34b model bash finetune 34b finetune lora sh if memory resources remain constrained consider employing q lora refer to the paper https arxiv org abs 2305 14314 an optimized solution that further reduces memory usage through the utilization of 4 bit quantized models and paged attention techniques to implement q lora execute the following scripts bash fine tuning the 7b model bash finetune 7b finetune qlora sh fine tuning the 34b model bash finetune 34b finetune qlora sh optimization effects below are the data on memory usage and training speed for the 7b and 34b models using full parameter fine tuning lora and qlora with different input lengths the evaluation was conducted on a machine equipped with an a100 sxm4 80g gpu utilizing cuda 12 1 and pytorch 2 1 the input length for the 7b model is 2048 and for the 34b model it is 4096 all tests were performed using a batch size of 4 and a gradient accumulation of 1 and both memory usage in gb and training speed in s iter were recorded the specific data is as follows table tr th model size th th method th th memory th th speed th tr tr th rowspan 3 7b th td sft td td 43 9g td td 2 67s iter td tr tr td lora td td 29 4g td td 2 04s iter td tr tr td q lora td td 19 9g td td 2 14s iter td tr tr th rowspan 1 34b th td q lora td td 37 7g td td 8 22s iter td tr table br br web ui please click the link to visit the official flagopen https flagopen baai ac cn website click on model trial dialogue model to fill out the application form after approval you can experience the dialogue capabilities of aquilachat2 online br br long context understanding aquilachat2 34b 16k is built on aquila2 34b processed by positional coding interpolation and sft on 200k high quality long text conversations dataset to extend the effective context window we tested the model four chinese and english long text quiz and summarization tasks from longbench https github com thudm longbench the evaluation results show that aquilachat2 34b 16k reaches the leading level of open source long text models close to gpt 3 5 16k br br tokenizer our tokenizer of bbpe type is trained on a 50gb text dataset mainly sampled from deduplicated pile and wudao corpus we also add some special tokens for passage and conversation separation br br faq you re welcome to submit your questions or share your user experience in github issues https github com flagai open aquila2 issues br br license agreement the aquila2 project is based on the apache 2 0 license https www apache org licenses license 2 0 the aquila2 series models are based on the baai aquila model license agreement assets aquila license pdf br br contact us if you are interested please join our wechat groups img src assets wechat qrcode jpg width 200 height 200 align center br br | llm llm-inference llm-training | ai |
visionscript | visionscript logo https visionscript dev assets full logo svg visionscript visionscript https visionscript dev is an abstract programming language for doing common computer vision tasks fast visionscript is built in python offering a simple syntax for running object detection classification and segmentation models read the documentation https visionscript dev docs view the demo https vimeo com 856043804 get started first install visionscript bash pip install visionscript you can then run visionscript using bash visionscript this will open a visionscript repl in which you can type commands run a file to run a visionscript file use bash visionscript your file vic use visionscript in a notebook visionscript offers an interactive web notebook through which you can run visionscript code to use the notebook run bash visionscript notebook this will open a notebook in your browser notebooks are ephermal you will need to copy your code to a file to save it quickstart find people in an image using object detection load photo jpg detect person say find people in all images in a folder using object detection in images detect person say replace people in a photo with an emoji load abbey jpg size say detect person replace emoji png save abbey2 jpg classify an image load photo jpg classify apple banana installation to install visionscript clone this repository and run pip install r requirements txt then make a file ending in vic in which to write your visionscript code when you have written your code run bash visionscript your file vic run in debug mode running in debug mode shows the full abstract syntax tree ast of your code bash visionscript your file vic showtree true debug mode is useful for debugging code while adding new features to the visionscript language inspiration the inspiration behind this project was to build a simple way of doing one off tasks consider a scenario where you want to run zero shot classification on a folder of images with visionscript you can do this in three lines of code in images classify cat dog say visionscript is not meant to be a full programming language for all vision tasks rather an abstract way of doing common tasks visionscript is ideal if you are new to concepts like classify and segment and want to explore what they do to an image syntax the syntax is inspired by both python and the wolfram language visionscript is an interpreted language run line by line like python statements use the format statement argument1 argument2 this is the same format as the wolfram language lexical inference and memory an i think unique feature in visionscript compared to other languages is lexical inference you don t need to declare variables to store images etc rather you can let visionscript do the work consider this example load photo jpg size say here size and say do not have any arguments rather they use the last input wolfram alpha has a feature to get the last input using visionscript uses the same concept but with a twist indeed size and say don t accept any arguments developer setup if you want to add new features or fix bugs in the visionscript language you will need to set up a developer environment to do so clone the language repository bash git clone https github com capjamesg visionscript then install the required dependencies and visionscript bash pip install r requirements txt pip install e now you can run visionscript using bash visionscript supported models visionscript provides abstract wrappers around clip https github com openai clip by openai classification ultralytics yolov8 https github com ultralytics ultralytics object detection training segmentation training fastsam https github com casia iva lab fastsam by casia iva lab segmentation groundedsam https docs autodistill com base models groundedsam object detection segmentation blip https github com salesforce blip caption generation vit https github com autodistill autodistill vit classification training license this project is licensed under an mit license license | computer-vision programming-languages visionscript | ai |
osx-re-101 | osx ios re 101 work in progress as i am actively collecting these must read https reverse put as https blog paloaltonetworks com tag mac os x https www synack com blog r d projects os x security research https pewpewthespells com re html https github com bx macho tools https github com kpwn iosre keep these handy osx mach o file format reference https pewpewthespells com re mach o file format pdf osx abi https pewpewthespells com re mac os x abi function calls pdf mach o structures https opensource apple com source xnu xnu 2050 18 24 external headers mach o loader h osx bsd system calls https sigsegv pl osx bsd syscalls https opensource apple com source xnu xnu 2050 18 24 bsd kern syscalls master basics universal binary the mach o file format https cocoaintheshell whine fr 2009 07 universal binary mach o format basics of the mach o file format https samhuri net posts 2010 01 basics of the mach o file format how os x executes applications http 0xfe blogspot de 2006 03 how os x executes applications html infecting mach o object format https papers put as papers macosx 2005 mach o infection ppt under the ihood https www defcon org images defcon 16 dc16 presentations defcon 16 hotchkies pdf dissection of minimal intel 32 bits 204 bytes mach o hello world executable file http seriot ch hello macho php crafting a tiny mach o executable http osxbook com blog 2009 03 15 crafting a tiny mach o executable parsing mach o files http lowlevelbits org parse mach o files elf vs mach o http timetobleed com dynamic linking elf vs mach o elf vs mach o 2 http timetobleed com dynamic symbol table duel elf vs mach o round 2 nasm hello world for x86 and x86 64 intel mac os x https gist github com filosottile 7125822 reverse engineering the os a practical guide https www youtube com watch v uqwh55yigyu malware anti debugging infection techniques obfuscation and encryption infecting mach o http nicolascormier com documentation security infecting mach o files pdf abusing the mach o format http cocoaintheshell com 2009 10 abusing mach o multi platform viruses made easy a case study http vxer org lib vjp00 html running executables on macos from memory https blog cylance com running executables on macos from memory macos execute from memory https github com prsecurity macos execute from memory blob master main c understanding apple s binary protection in mac os x http osxbook com book bonus chapter7 binaryprotection macs get sick too http www irongeek com i php page videos derbycon6 104 macs get sick too tyler halfpop jacob soo a peek under the hood of ios malware http webdiis unizar es ricardo files papers gr wma 16 pdf crafting macos rootkits https www zdziarski com blog wp content uploads 2017 02 crafting macos root kits pdf revisiting mac os x kernel rootkits http phrack org issues 69 7 html article methods of malware persistence on mac os x https www virusbulletin com uploads pdf conference vb2014 vb2014 wardle pdf let s play practical os x malware detection analysis https www synack com wp content uploads 2016 03 rsa osx malware pdf various research tutorials reversing and keygenning qwertyoruiop s crackme https reverse put as 2018 10 06 reversing and keygenning qwertyoruiop crackme cracking tutorial 1 sandwich crackme http reverse put as wp content uploads 2012 06 sandwich crackme tut qwertyoruiop txt solving crackmes with ldpreload http radare today solving crackmes with ldpreload analyzing binaries with hopper s decompiler http abad1dea tumblr com post 23487860422 analyzing binaries with hoppers decompiler reverse engineering hopper disassembler v3 9 9 https www youtube com watch v pcitclqgs9q reverse engineering ios apps hacking on lyft https realm io news conrad kramer reverse engineering ios apps lyft jailbreak ios 8 1 2 and analyze related exploits http proteaswang blogspot com 2017 04 jailbreak ios 812 and analyze related html attacking the xnu kernel in el capitan https www blackhat com docs eu 15 materials eu 15 todesco attacking the xnu kernal in el capitain pdf shooting the osx el capitan kernel like a sniper https speakerdeck com flankerhqd shooting the osx el capitan kernel like a sniper the italian morons are back what are they up to this time https reverse put as 2016 02 29 the italian morons are back what are they up to this time the journey of a complete osx privilege escalation with a single vulnerability part 1 http keenlab tencent com en 2016 07 29 the journey of a complete osx privilege escalation with a single vulnerability part 1 ios 10 kernel heap revisted http gsec hitb org materials sg2016 d2 20 20stefan 20esser 20 20ios 2010 20kernel 20heap 20revisited pdf who needs decrypted kernels anyways http blog offcellresearch com security apple ios kernel 2016 08 23 who needs decrypted kernels anyways html mac os x privilege escalation via use after free cve 2016 1828 https bazad github io 2016 05 mac os x use after free pegasus ios kernel vulnerability explained http sektioneins de en blog 16 09 02 pegasus ios kernel vulnerability explained html behind the scenes with ios security https www blackhat com docs us 16 materials us 16 krstic pdf the apple sandbox deeper into the quagmire https www youtube com watch v mg715hcdgo8 a deep dive into the many flavors of ipc available on os x https vimeo com 127859750 analysis of ios 9 3 3 jailbreak security enhancements of ios 10 http powerofcommunity net poc2016 pangu pdf fried apples jailbreak diy https speakerdeck com mbazaliy fried apples jailbreak diy reversing a macos kernel extension dsmos http lightbulbone com 2016 10 04 intro to macos kernel debugging html demystifying the secure enclave processor http mista nu research sep paper pdf leveraging apple s game engine to detect macos threats https objectivebythesea com v1 talks obts v1 malm stein pdf get cozy with openbsm auditing https objective see com talks wardle shmoocon2018 pdf real time auditing on macos with openbsm https meliot me 2017 07 02 mac os real time auditing kernel extension kext development kext controls and notifications https developer apple com library content documentation darwin conceptual nkeconceptual control control html network kernel extensions reference https developer apple com library content documentation darwin conceptual nkeconceptual reference reference html apple ref doc uid tp40001858 ch232 bbaggged working with trustedbsd in mac os x https sysdev me trusted bsd in osx building an apple osx kernel module with cmake c c http www goodbits ca index php 2017 09 25 building an apple osx kernel module with cmake cc debugging macos kernel using virtualbox https klue github io blog 2017 04 macos kernel debugging vbox remote kext debugging https rednaga io 2017 04 09 remote kext debugging introduction to macos kernel debugging https lightbulbone com posts 2016 10 intro to macos kernel debugging kernel debugging with lldb and vmware fusion http ddeville me 2015 08 kernel debugging with lldb and vmware fusion monitoring process creation via the kernel part i https objective see com blog html blogentry9 monitoring process creation via the kernel part ii https objective see com blog blog 0x0a html monitoring process creation via the kernel part iii https objective see com blog blog 0x0b html monitoring macos part i monitoring process execution via macf https www fortinet com blog threat research monitoring macos part i monitoring process execution via macf html monitoring macos part ii monitoring file system events and dylib loading via macf https www fortinet com blog threat research monitor file system events and dylib loading via macf on macos html monitoring macos part iii monitoring network activities using socket filters https www fortinet com blog threat research monitoring macos part iii monitoring network activities using html a binary whitelisting blacklisting system for mac os x https github com google santa other the python bites your apple fuzzing and exploiting osx kernel bugs https speakerdeck com flankerhqd the python bites your apple fuzzing and exploiting osx kernel bugs artefacts and tricks for mac os x http sud0man blogspot fr 2015 05 artefacts for mac os x html m 1 collection of forensics artifacs location for mac os x and ios https github com pstirparo mac4n6 new macos sierra 10 12 forensic artifacts introducing unified logging https www mac4n6 com blog 2016 11 13 new macos sierra 1012 forensic artifacts introducing unified logging a curated list of shell commands and tools specific to os x https github com herrbischoff awesome osx command line os x security and privacy guide https github com drduh os x security and privacy guide a launchd tutorial http launchd info https objective see com index html os x malloc introspection tool https github com blankwall macheap macos hardening guide http newosxbook com files moxii3 appendixa pdf by jonathan levin checkout4mac http sud0man blogspot sk 2016 10 new version of checkout4mac 02 html osx kernel fuzzer https github com silvermoonsecurity passivefuzzframeworkosx ios instrumentation without jailbreak https www nccgroup trust uk about us newsroom and events blogs 2016 october ios instrumentation without jailbreak macos monitoring the open source way https blogs dropbox com tech 2018 04 4696 mac os x el capitan 10 11 and task for pid https attilathedud me mac os x el capitan 10 11 and task for pid crackmes and challenges https reverse put as crackmes exercises section in http beginners re reverse engineering for beginners en pdf books the mac hacker s handbook by charlie miller dino dai zovi mac os x and ios internals to the apple s core by jonathan levin mac os x internals a systems approach by amit singh ios app reverse engineering https github com iosre iosappreverseengineering ios hacker s handbook by charlie miller dion blazakis dino dai zovi stefan esser vincenzo iozzo ralf philip weinmann hacking and securing ios applications by jonathan zdziarski | os |
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ionic_firebase_crud_shopping_list_app | magic of ionic firebase together main components ionic3 angularfire2 how to run the project clone the repo cd into it run npm i and then ionic lab open a browser and navigate to localhost 8100 ionic | ionic3 angularfire2 firebase nodejs crud-application | server |
ENTMDM | entmdm master data is the critical information about business entities and concepts such as customers suppliers employees patients products materials stores plants recipes and so on it is the foundational information used throughout an organization and make key business decisions master data management mdm is the set of technologies and processes which allow business and technology users manage govern and analyze this critical information across the enterprise this app demonstrates pharma and medical segments within cardinal health author duplicate customer master data due to their segment specific view and enterprise master data stewards de duplicating the segment specific records into a unified golden customer record that is ready for consumption by the down stream systems this application was generated using jhipster 4 3 0 you can find documentation and help at https jhipster github io documentation archive v4 3 0 https jhipster github io documentation archive v4 3 0 development before you can build this project you must install and configure the following dependencies on your machine 1 node js we use node to run a development web server and build the project depending on your system you can install node either from source or as a pre packaged bundle 2 yarn we use yarn to manage node dependencies depending on your system you can install yarn either from source or as a pre packaged bundle after installing node you should be able to run the following command to install development tools you will only need to run this command when dependencies change in package json package json yarn install we use gulp as our build system install the gulp command line tool globally with yarn global add gulp cli run the following commands in two separate terminals to create a blissful development experience where your browser auto refreshes when files change on your hard drive gradlew gulp bower is used to manage css and javascript dependencies used in this application you can upgrade dependencies by specifying a newer version in bower json bower json you can also run bower update and bower install to manage dependencies add the h flag on any command to see how you can use it for example bower update h for further instructions on how to develop with jhipster have a look at using jhipster in development building for production to optimize the entmdm application for production run gradlew pprod clean bootrepackage this will concatenate and minify the client css and javascript files it will also modify index html so it references these new files to ensure everything worked run java jar build libs war then navigate to http localhost 8080 http localhost 8080 in your browser refer to using jhipster in production for more details testing to launch your application s tests run gradlew test client tests unit tests are run by karma and written with jasmine they re located in src test javascript src test javascript and can be run with gulp test for more information refer to the running tests page using docker to simplify development optional you can use docker to improve your jhipster development experience a number of docker compose configuration are available in the src main docker src main docker folder to launch required third party services for example to start a mysql database in a docker container run docker compose f src main docker mysql yml up d to stop it and remove the container run docker compose f src main docker mysql yml down you can also fully dockerize your application and all the services that it depends on to achieve this first build a docker image of your app by running gradlew bootrepackage pprod builddocker then run docker compose f src main docker app yml up d for more information refer to using docker and docker compose this page also contains information on the docker compose sub generator yo jhipster docker compose which is able to generate docker configurations for one or several jhipster applications continuous integration optional to configure ci for your project run the ci cd sub generator yo jhipster ci cd this will let you generate configuration files for a number of continuous integration systems consult the setting up continuous integration page for more information jhipster homepage and latest documentation https jhipster github io jhipster 4 3 0 archive https jhipster github io documentation archive v4 3 0 using jhipster in development https jhipster github io documentation archive v4 3 0 development using docker and docker compose https jhipster github io documentation archive v4 3 0 docker compose using jhipster in production https jhipster github io documentation archive v4 3 0 production running tests page https jhipster github io documentation archive v4 3 0 running tests setting up continuous integration https jhipster github io documentation archive v4 3 0 setting up ci node js https nodejs org yarn https yarnpkg org bower http bower io gulp http gulpjs com browsersync http www browsersync io karma http karma runner github io jasmine http jasmine github io 2 0 introduction html protractor https angular github io protractor leaflet http leafletjs com definitelytyped http definitelytyped org | masterdata | server |
mBed-Collision_Avoidance_BT_Car | collision avoidance robot with custom bluetooth controller the objective of this project is to control a robot vehicle using a custom designed controller both the robot and controller are powered by the mbed lpc1768 and connected by two hc 05 bluetooth modules the robot using an ultrasonic sensor to detect obstacles within 250 mm of its front the bluetooth communication uses a protocol similar to the stop and wait arq flow control protocol when sending distance readings and acknowledgements back and forth between the vehicle and controller during the collision avoidance routine demo video see a short demo of the robot in action in this video https www youtube com watch v 5chwjklpg6u parts list robot shadow robot kit mbed lpc1768 hc 05 bluetooth module h bridge motor drive reversible dc motor x2 hc sr04 ultrasonic sensor ambient light sensor headlights breadboard leds barrel jack adapters x2 battery packs x2 aa batteries x8 duct tape breadboards x2 controller mbed lpc1768 hc 05 bluetooth module ulcd 144 g2 128x128 smart color lcd sparkfun analog joystick breadboard speaker mini usb cable either a battery pack or pc for power assembly instuctions for the shadow robot kit can be found here https www youtube com watch v ajrytqzu5oe wiring controller hc 05 bluetooth module the bluetooth module is used to communicate with the robot joystick readings are sent until the collision protocol is entered at which point the robot sends distance readings describing its surroundings and the controller sends acknowledgements back to indicate the readings were received and processed mbed hc 05 p9 rx p10 tx 5v vcc gnd gnd gnd en ulcd the lcd screen is used to draw the radar view of the robots surroundings upon entering the collision protocol mbed ulcd p28 rx p27 tx 5v vcc gnd gnd p11 res analog joystick the joystick contains two potentiometers which are used to control the robots movements mbed joystick p15 vert p16 horz vout 3 3v vcc gnd gnd p17 sel robot hc 05 bluetooth module the bluetooth module is used to communicate with the robot joystick readings are sent until the collision protocol is entered at which point the robot sends distance readings describing its surroundings and the controller sends acknowledgements back to indicate the readings were received and processed mbed hc 05 p9 rx p10 tx 5v vcc gnd gnd gnd en reversible dc motors used to move the robot dc motor h bridge driver mbed left a01 left a02 right b01 right b02 pwma p21 pwmb p22 ain1 p15 ain2 p16 bin1 p17 bin2 p19 gnd all 3 gnd vcc vout 3 3v stby vout 3 3v vm 5v battery pack hc sr04 ultrasonic sensor the ultrasonic sensor is used to control the collision avoidance protocol and to read in radar data to be sent back to the controller over bluetooth hc sr04 mbed trig p5 echo p6 vcc 5v battery pack gnd gnd automatic headlights ambient light sensor mbed headlights leds vcc vout 3 3v gnd gnd sig p20 p30 setting up the hc 05 s use this https os mbed com users edodm85 code hc05 at mode code to send at commands to the hc 05 bluetooth modules see this https howtomechatronics com tutorials arduino how to configure pair two hc 05 bluetooth module master slave commands arduino tutorial for the commands to configure both the master and slave hc 05 note the enable pin must be wired to high to enter command mode on the hc 05s after they are configured the pin should be wired to ground authors this project was designed and prototyped by both myself andres rodriguez and david prina as our final project for embedded systems design this repository is a mirror of the original mbed notebook page i made as part of this project see the original mbed notebook page here https os mbed com users andres013 notebook collision avoidance robot with bluetooth controlle | os |
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django-study-managements | 2 1 2 3 source bin activate 4 requirements txt pip install r requirements txt 5 auth json dump json manage py loaddata fixtures json 6 manage py migrate 7 manage py runserver | server |
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JRC-AoI | jrc aoi doi https zenodo org badge 370934498 svg https zenodo org badge latestdoi 370934498 supplementary material for the following papers ol li j lee d niyato y l guan and d i kim learning to schedule joint radar communication with deep multi agent reinforcement learning in ieee transactions on vehicular technology vol 71 no 1 pp 406 422 jan 2022 doi 10 1109 tvt 2021 3124810 https ieeexplore ieee org abstract document 9601214 li li j lee d niyato y l guan and d i kim learning to schedule joint radar communication requests for optimal information freshness 2021 ieee intelligent vehicles symposium iv 2021 pp 8 15 doi 10 1109 iv48863 2021 9575131 https ieeexplore ieee org abstract document 9575131 also available on digital repository of ntu https hdl handle net 10356 150718 li ol getting started install the dependencies listed in requirements txt running experiments the dqn training process as well as the 1 step planning and round robin baseline algorithms may be run from the command line examples are provided below dqn python dqn jrc aoi d py obj avg rd bad2b 0 1 0 2 0 1 w radar 9 10 1 w ovf 1 data gen 2 3 1 nn size 64 64 double dueling n experiments 5 1 step planner python test jrc aoi2b py obj avg mode best rd bad2b 0 1 0 2 0 1 w radar 9 10 1 data gen 2 3 1 n experiments 5 round robin python test jrc aoi2b py obj avg mode rotate rd bad2b 0 1 0 2 0 1 w radar 9 10 1 data gen 2 3 1 n experiments 5 | server |
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Applied-Deep-Learning-and-Computer-Vision-for-Self-Driving-Cars | applied deep learning and computer vision for self driving cars a href https www packtpub com in data hands on self driving cars with deep learning utm source github utm medium repository utm campaign 9781838646301 img src https www packtpub com media catalog product cache c2dd93b9130e9fabaf187d1326a880fc 9 7 9781838646301 original 42 jpeg alt applied deep learning and computer vision for self driving cars height 256px align right a this is the code repository for applied deep learning and computer vision for self driving cars https www packtpub com in data hands on self driving cars with deep learning utm source github utm medium repository utm campaign 9781838646301 published by packt build autonomous vehicles using deep neural networks and behavior cloning techniques what is this book about this book covers the following exciting features implement deep neural network from scratch using the keras library understand the importance of deep learning in self driving cars get to grips with feature extraction techniques in image processing using the opencv library design a software pipeline that detects lane lines in videos implement a convolutional neural network cnn image classifier for traffic signal signs if you feel this book is for you get your copy https www amazon com dp 1838646302 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a errata page 22 it is radar has great resolution should be lidar has great resolution it is let s look at the following radar chart should be let s look at the following lidar chart it is rfig 1 5 radar chart strength should be fig 1 5 lidar chart strength page 23 it is fig 1 6 radar and camera plot should be fig 1 6 lidar and camera plot page 30 it is one of the shortcomings of lidar is that it usually has a low resolution should be one of the shortcomings of radar is that it usually has a low resolution instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following import numpy as np import matplotlib pyplot as plt import tensorflow as tf from tensorflow import keras from tensorflow keras import layers in the book you will also learn important python libraries like keras for deep learning and opencv for computer vision in detail all codes are tested on latest anaconda environment https www anaconda com products individual with python 3 7 on windows 16 gb laptop it is recommended to use laptop with more than 8gb you can also use google colab if you would like to execute the code in platform independent environment you need to install below libraries 1 tensorflow installation https www tensorflow org install 2 keras installation https keras io 3 pandas installation https pandas pydata org 4 numpy installation https numpy org 5 opencv installation https stackoverflow com questions 51853018 how do i install opencv using pip 56315658 note you can directly install anaconda environment as it will install most of the datascience packages at once https www anaconda com products individual any additional installation instructions and information the user needs for getting set up for few of the chapter you need to install few files and put in folder provides please find the details below chapter 3 to 6 no download required chapter 7 download link is provided in folder traffic signs data download the file and put in traffic signs data chapter 8 9 no download required chapter 10 download link is provided in beta simulator windows and track folder download the files and put in these folders chapter 11 download link can be found in data folder following is what you need for this book if you are a deep learning engineer ai researcher or anyone looking to implement deep learning and computer vision techniques to build self driving blueprint solutions this book is for you anyone who wants to learn how various automotive related algorithms are built will also find this book useful python programming experience along with a basic understanding of deep learning is necessary to get the most of this book with the following software and hardware list you can run all code files present in the book chapter 1 12 software and hardware list chapter software required os required 1 install python 3 7 with latest anaconda environment windows mac os x and linux any 2 install deep learning libraries tensorflow 2 0 and keras 2 3 4 windows mac os x and linux any 3 install image processing library opencv windows mac os x and linux any we also provide a pdf file that has color images of the screenshots diagrams used in this book https static packt cdn com downloads 9781838646301 colorimages pdf related products other books you may enjoy pytorch computer vision cookbook packt https www packtpub com in data pytorch computer vision cookbook utm source github utm medium repository utm campaign 9781838644833 amazon https www amazon com dp 1838644830 mastering computer vision with tensorflow 2 x packt https www packtpub com in data advanced computer vision with tensorflow 2 x utm source github utm medium repository utm campaign 9781838827069 amazon https www amazon com dp 1838827064 get to know the authors sumit ranjan is a silver medalist in his bachelor of technology electronics and telecommunication degree he is a passionate data scientist who has worked on solving business problems to build an unparalleled customer experience across domains such as automobile healthcare semi conductor cloud virtualization and insurance he is experienced in building applied machine learning computer vision and deep learning solutions to meet real world needs he was awarded autonomous self driving car scholar by kpit technologies he has also worked on multiple research projects at mercedes benz research and development apart from work his hobbies are traveling and exploring new places wildlife photography and blogging dr s senthamilarasu was born and raised in the coimbatore tamil nadu he is a technologist designer speaker storyteller journal reviewer educator and researcher he loves to learn new technologies and solves real world problems in the it industry he has published various journals and research papers and has presented at various international conferences his research areas include data mining image processing and neural network he loves reading tamil novels and involves himself in social activities he has also received silver medals in international exhibitions for his research products for children with an autism disorder he currently lives in bangalore and is working closely with lead clients suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781838646301 https packt link free ebook 9781838646301 a p | ai |
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flang | flang flang also known as classic flang is an out of tree fortran compiler targeting llvm it is an open sourced version of pgfortran a commercial fortran compiler from pgi nvidia it is different from the new flang formerly known as f18 see https flang llvm org which has been part of the llvm project since 2020 although both are developed by the same community it is also unrelated to other projects of the same name such as https github com llvm flang flang and https github com isanbard flang classic flang is used in several downstream commercial projects like the amd https developer amd com amd aocc arm https www arm com products development tools server and hpc allinea studio fortran compiler and huawei https support huaweicloud com intl en us ug bisheng kunpengdevps kunpengbisheng 06 0001 html compilers and continues to be maintained but the plan is to replace classic flang with the new flang in the future visit the flang wiki for more information https github com flang compiler flang wiki to sign up for the developer mailing lists for announcements and discussions visit https lists llvm org cgi bin mailman listinfo flang dev we have a flang compiler channel on slack slack is invitation only but anyone can join with the invitation link below https join slack com t flang compiler shared invite mjexoteymzq3mjixlte0otk4nzqynzutodqzzweymjkwyw building flang instructions for building flang can be found on the flang wiki https github com flang compiler flang wiki building flang compiler options for a list of compiler options enter flang help flang accepts all clang compiler options and supports many as well as the following fortran specific compiler options lang none noflanglibs do not link against flang libraries mp enable openmp and link with with openmp library libomp nomp do not link with openmp library libomp mbackslash treat backslash in quoted strings like any other character mnobackslash treat backslash in quoted strings like a c style escape character default mbyteswapio swap byte order for unformatted input output mfixed assume fixed format source mextend allow source lines up to 132 characters mfreeform assume free format source mpreprocess run preprocessor for fortran files mrecursive generate code to allow recursive subprograms mstandard check standard conformance msave assume all variables have save attribute module path to module file i also works mallocatable 95 select fortran 95 semantics for assignments to allocatable objects mallocatable 03 select fortran 03 semantics for assignments to allocatable objects default static flang libs link using static flang libraries m no daz treat denormalized numbers as zero m no flushz set sse to flush to zero mode mcache align align large objects on cache line boundaries m no fprelaxed this option is ignored fdefault integer 8 treat integer and logical as integer 8 and logical 8 fdefault real 8 treat real as real 8 i8 treat integer and logical as integer 8 and logical 8 r8 treat real as real 8 fno fortran main don t link in fortran main | fortran compiler llvm fortran-compiler | front_end |
Smart_Contacts | smart contacts this repository is dedicated to code for alogrand smart contracts using choice coin read docs for how to implement algogenous smart contracts for your own applications smart contracts are programs that automatically execute transferring cryptocurrency between parties in other words smart contracts are logically executed on a blockchain to transfer assets without only computational or formalized oversight algorand smart contracts ascs allow for global payments and financial transactions with instantaneous processing and only marginal fees less than 0 01 in total value as typically described the three types of ascs are 1 stateful smart contracts 2 stateless smart contracts and 3 algogeneous smart contracts overviw on choice coin smart contracts https forum algorand org t choice coin smart contracts 6798 stateful stateful refers to the contract s ability to store information in a specific state on the network for example one type of stateful smart contract is a request payment function allowing a user to request payment from another user generally stateful smart contracts are logical programs which store data on the blockchain stateless stateless smart contracts differ in that they validate transactions between parties like an escrow and more like a contract in the traditional sense stateless smart contracts on the algorand network also act as signature delegators signing transactions thus validating them on the main blockchain network by analogy many describe stateless smart contracts as essentially equivalent to escrow functions indeed the essential design purpose for stateless smart contracts were to approve or deny blockchain transactions algogeneous representing a technical convergence of stateless and stateful smart contracts algogeneous smart contracts include an innovative integration with artificial intelligence where previous ascs must be stateful or stateless algogeneous contracts may be stateful stateless or both | blockchain |
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Machine-Learning-Projects | machine learning projects list of machine learning computer vision and natural language processing projects | ai |
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octoxbps | this is octoxbps a powerful xbps front end using qt libs main window https raw githubusercontent com aarnt octoxbps master octoxbps mainwindow png octoxbps is a qt based gui front end to the xbps https github com void linux xbps package manager derived from octopkg http tintaescura com projects octopkg it consists of a lxqt sudo clone called octoxbps sudo https github com aarnt octoxbps tree master sudo used to gain root privileges a package browser application used to search install remove and update packages and a system tray icon notifier https github com aarnt octoxbps tree master notifier to notify the user about package changes the project is compatible with void linux https voidlinux org you can use xbps to install the latest octoxbps version available in your distro xbps install s octoxbps follow the steps bellow to compile the latest source code you ll need curl git and qt5 packages git clone https github com aarnt octoxbps cd octoxbps sudo qmake qt5 make make install cd notifier qmake qt5 make make install cd qmake qt5 make make install in order to run octoxbps main package browser octoxbps to execute octoxbps system tray notifier octoxbps notifier you ll also need curl and sudo packages installed and your user to be a member of the wheel group enjoy | xbps voidlinux trident qt | front_end |
Gridder | gridder are you tired of struggling to create complex grid based layouts for your web pages do you want to design beautiful responsive websites without having to write a single line of code if so gridder io is the perfect solution for you whether you re building a simple landing page or a complex e commerce site gridder io has everything you need to bring your vision to life the framework is based on css flexbox which means you can create complex grid layouts without any coding knowledge check out https gridder io https gridder io for documentation and details changelog v2 2 1 new add demo website to download v2 2 0 new add complete new masonry grid option add support for reverse ordering on all breakpoints improvements optimize readability of src for better costumization change the default huge breakpoint from 125em to 122em add shorthand for masonry classes v2 1 1 fix fix bug where eleventh column wouldn t take up the correct amount of space on some breakpoints v2 1 0 new new huge breakpoint to support wide monitors v2 0 0 new new base flexgrid layout containing 4 breakpoints | front_end |
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vecstack | pypi version https img shields io pypi v vecstack svg colorb 4cc61e https pypi python org pypi vecstack pypi license https img shields io pypi l vecstack svg https github com vecxoz vecstack blob master license txt build status https travis ci org vecxoz vecstack svg branch master https travis ci org vecxoz vecstack coverage status https coveralls io repos github vecxoz vecstack badge svg branch master https coveralls io github vecxoz vecstack branch master pypi pyversions https img shields io pypi pyversions vecstack svg https pypi python org pypi vecstack vecstack python package for stacking stacked generalization featuring lightweight functional api and fully compatible scikit learn api convenient way to automate oof computation prediction and bagging using any number of models functional api https github com vecxoz vecstack usage functional api minimalistic get your stacked features in a single line ram friendly the lowest possible memory consumption kaggle ready stacked features and hyperparameters from each run can be automatically saved https github com vecxoz vecstack blob master vecstack core py l209 in files no more mess at the end of the competition log example https github com vecxoz vecstack blob master examples 03 log example txt scikit learn api https github com vecxoz vecstack usage scikit learn api standardized fully scikit learn compatible transformer class exposing fit and transform methods pipeline certified implement and deploy multilevel stacking https github com vecxoz vecstack blob master examples 04 sklearn api regression pipeline ipynb like it s no big deal using sklearn pipeline pipeline and of course featureunion is also invited to the party overall specs use any sklearn like estimators perform classification and regression https github com vecxoz vecstack blob master vecstack coresk py l83 tasks predict class labels or probabilities https github com vecxoz vecstack blob master vecstack coresk py l119 in classification task apply any user defined metric https github com vecxoz vecstack blob master vecstack coresk py l124 apply any user defined transformations https github com vecxoz vecstack blob master vecstack coresk py l87 for target and prediction python 3 5 and higher unofficial support for python 2 7 and 3 4 https github com vecxoz vecstack blob master py2 md win linux mac mit license https github com vecxoz vecstack blob master license txt depends on numpy scipy scikit learn 0 18 get started faq https github com vecxoz vecstack stacking faq installation guide https github com vecxoz vecstack installation usage functional api https github com vecxoz vecstack usage functional api scikit learn api https github com vecxoz vecstack usage scikit learn api tutorials stacking concept pictures stacking implementation from scratch https github com vecxoz vecstack blob master examples 00 stacking concept pictures code ipynb examples all examples are valid for both api with little difference in parameters https github com vecxoz vecstack 21 how do parameters of stacking function and stackingtransformer correspond functional api regression https github com vecxoz vecstack blob master examples 01 regression ipynb classification with class labels https github com vecxoz vecstack blob master examples 02 classification with class labels ipynb classification with probabilities detailed workflow https github com vecxoz vecstack blob master examples 03 classification with proba detailed workflow ipynb scikit learn api regression multilevel stacking using pipeline https github com vecxoz vecstack blob master examples 04 sklearn api regression pipeline ipynb documentation functional api https github com vecxoz vecstack blob master vecstack core py l133 or type help stacking scikit learn api https github com vecxoz vecstack blob master vecstack coresk py l64 or type help stackingtransformer installation note python 3 5 or higher is required if you re still using python 2 7 or 3 4 see installation details here https github com vecxoz vecstack blob master py2 md classic 1st time installation recommended pip install vecstack install for current user only if you have some troubles with write permission pip install user vecstack if your path doesn t work usr bin python m pip install vecstack c python36 python m pip install vecstack upgrade vecstack and all dependencies pip install upgrade vecstack upgrade vecstack without upgrading dependencies pip install upgrade no deps vecstack upgrade directly from github without upgrading dependencies pip install upgrade no deps https github com vecxoz vecstack archive master zip uninstall pip uninstall vecstack usage functional api python from vecstack import stacking get your data initialize 1st level estimators models linearregression ridge random state 0 get your stacked features in a single line s train s test stacking models x train y train x test regression true verbose 2 use 2nd level estimator with stacked features usage scikit learn api python from vecstack import stackingtransformer get your data initialize 1st level estimators estimators lr linearregression ridge ridge random state 0 initialize stackingtransformer stack stackingtransformer estimators regression true verbose 2 fit stack stack fit x train y train get your stacked features s train stack transform x train s test stack transform x test use 2nd level estimator with stacked features stacking faq 1 how can i report an issue how can i ask a question about stacking or vecstack package https github com vecxoz vecstack 1 how can i report an issue how can i ask a question about stacking or vecstack package 2 how can i say thanks https github com vecxoz vecstack 2 how can i say thanks 3 how to cite vecstack https github com vecxoz vecstack 3 how to cite vecstack 4 what is stacking https github com vecxoz vecstack 4 what is stacking 5 what about stacking name https github com vecxoz vecstack 5 what about stacking name 6 do i need stacking at all https github com vecxoz vecstack 6 do i need stacking at all 7 can you explain stacking stacked generalization in 10 lines of code https github com vecxoz vecstack 7 can you explain stacking stacked generalization in 10 lines of code 8 why do i need complicated inner procedure for stacking https github com vecxoz vecstack 8 why do i need complicated inner procedure for stacking 9 i want to implement stacking stacked generalization from scratch can you help me https github com vecxoz vecstack 9 i want to implement stacking stacked generalization from scratch can you help me 10 what is oof https github com vecxoz vecstack 10 what is oof 11 what are estimator learner model https github com vecxoz vecstack 11 what are estimator learner model 12 what is blending how is it related to stacking https github com vecxoz vecstack 12 what is blending how is it related to stacking 13 how to optimize weights for weighted average https github com vecxoz vecstack 13 how to optimize weights for weighted average 14 what is better weighted average for current level or additional level https github com vecxoz vecstack 14 what is better weighted average for current level or additional level 15 what is bagging how is it related to stacking https github com vecxoz vecstack 15 what is bagging how is it related to stacking 16 how many models should i use on a given stacking level https github com vecxoz vecstack 16 how many models should i use on a given stacking level 17 how many stacking levels should i use https github com vecxoz vecstack 17 how many stacking levels should i use 18 how do i choose models for stacking https github com vecxoz vecstack 18 how do i choose models for stacking 19 i am trying hard but still can t beat my best single model with stacking what is wrong https github com vecxoz vecstack 19 i am trying hard but still cant beat my best single model with stacking what is wrong 20 what should i choose functional api stacking function or scikit learn api stackingtransformer https github com vecxoz vecstack 20 what should i choose functional api stacking function or scikit learn api stackingtransformer 21 how do parameters of stacking function and stackingtransformer correspond https github com vecxoz vecstack 21 how do parameters of stacking function and stackingtransformer correspond 22 why scikit learn api was implemented as transformer and not predictor https github com vecxoz vecstack 22 why scikit learn api was implemented as transformer and not predictor 23 how to estimate stacking training time and number of models which will be built https github com vecxoz vecstack 23 how to estimate stacking training time and number of models which will be built 24 which stacking variant should i use a oof pred bag or b oof pred https github com vecxoz vecstack 24 which stacking variant should i use a oof pred bag or b oof pred 25 how to choose number of folds https github com vecxoz vecstack 25 how to choose number of folds 26 when i transform train set i see train set was detected what does it mean https github com vecxoz vecstack 26 when i transform train set i see train set was detected what does it mean 27 how is the very first stacking level called l0 or l1 where does counting start https github com vecxoz vecstack 27 how is the very first stacking level called l0 or l1 where does counting start 28 can i use randomized gridsearchcv to tune the whole stacking pipeline https github com vecxoz vecstack 28 can i use randomizedgridsearchcv to tune the whole stacking pipeline 29 how to define custom metric especially auc https github com vecxoz vecstack 29 how to define custom metric especially auc 30 do folds splits have to be the same across estimators and stacking levels how does random state work https github com vecxoz vecstack 30 do folds splits have to be the same across estimators and stacking levels how does random state work 1 how can i report an issue how can i ask a question about stacking or vecstack package just open an issue here https github com vecxoz vecstack issues ask me anything on the topic i m a bit busy so typically i answer on the next day 2 how can i say thanks just give me a star in the top right corner of the repository page 3 how to cite vecstack misc vecstack2016 author igor ivanov title vecstack year 2016 publisher github howpublished url https github com vecxoz vecstack 4 what is stacking stacking stacked generalization is a machine learning ensembling technique main idea is to use predictions as features more specifically we predict train set in cv like fashion and test set using some 1st level model s and then use these predictions as features for 2nd level model you can find more details concept pictures code in stacking tutorial https github com vecxoz vecstack blob master examples 00 stacking concept pictures code ipynb also make sure to check out ensemble learning https en wikipedia org wiki ensemble learning stacking https en wikipedia org wiki ensemble learning stacking in wikipedia classical kaggle ensembling guide https mlwave com kaggle ensembling guide stacked generalization https www researchgate net publication 222467943 stacked generalization paper by david h wolpert 5 what about stacking name often it is also called stacked generalization the term is derived from the verb to stack to put together to put on top of each other it implies that we put some models on top of other models i e train some models on predictions of other models from another point of view we can say that we stack predictions in order to use them as features 6 do i need stacking at all it depends on specific business case the main thing to know about stacking is that it requires significant computing resources no free lunch theorem https en wikipedia org wiki there ain 27t no such thing as a free lunch applies as always stacking can give you an improvement but for certain price deployment computation maintenance only experiment for given business case will give you an answer is it worth an effort and money at current point large part of stacking users are participants of machine learning competitions on kaggle you can t go too far without ensembling i can secretly tell you that at least top half of leaderboard in pretty much any competition uses ensembling stacking in some way stacking is less popular in production due to time and resource constraints but i think it gains popularity 7 can you explain stacking stacked generalization in 10 lines of code of course https github com vecxoz vecstack blob master examples 00 stacking concept pictures code ipynb 8 why do i need complicated inner procedure for stacking i can just do the following why not python model l1 xgbregressor model l1 model l1 fit x train y train s train model l1 predict x train reshape 1 1 does not work due to overfitting must be cv s test model l1 predict x test reshape 1 1 model l2 linearregression model l2 model l2 fit s train y train final prediction model l2 predict s test code above will give meaningless result if we fit on x train we can t just predict x train because our 1st level model has already seen x train and its prediction will be overfitted to avoid overfitting we perform cross validation procedure and in each fold we predict out of fold oof part of x train you can find more details concept pictures code in stacking tutorial https github com vecxoz vecstack blob master examples 00 stacking concept pictures code ipynb 9 i want to implement stacking stacked generalization from scratch can you help me not a problem https github com vecxoz vecstack blob master examples 00 stacking concept pictures code ipynb 10 what is oof oof is abbreviation for out of fold prediction it s also known as oof features stacked features stacking features etc basically it means predictions for the part of train data that model haven t seen during training 11 what are estimator learner model basically it is the same thing meaning machine learning algorithm often these terms are used interchangeably speaking about inner stacking mechanics you should remember that when you have single 1st level model there will be at least n folds separate models trained in each cv fold on different subsets of data see q23 https github com vecxoz vecstack 23 how to estimate stacking training time and number of models which will be built for more details 12 what is blending how is it related to stacking basically it is the same thing both approaches use predictions as features often this terms are used interchangeably the difference is how we generate features predictions for the next level stacking perform cross validation procedure and predict each part of train set oof blending predict fixed holdout set vecstack package supports only stacking i e cross validation approach for given random state value e g 42 folds splits will be the same across all estimators see also q30 https github com vecxoz vecstack 30 do folds splits have to be the same across estimators and stacking levels how does random state work 13 how to optimize weights for weighted average you can use for example scipy optimize minimize scipy optimize differential evolution 14 what is better weighted average for current level or additional level by default you can start from weighted average it is easier to apply and more chances that it will give good result then you can try additional level which potentially can outperform weighted average but not always and not in an easy way experiment is your friend 15 what is bagging how is it related to stacking bagging https en wikipedia org wiki bootstrap aggregating or bootstrap aggregating works as follows generate subsets of training set train models on these subsets and then find average of predictions also term bagging is often used to describe following approaches train several different models on the same data and average predictions train same model with different random seeds on the same data and average predictions so if we run stacking and just average predictions it is bagging 16 how many models should i use on a given stacking level note 1 the best architecture can be found only by experiment note 2 always remember that higher number of levels or models does not guarantee better result the key to success in stacking and ensembling in general is diversity low correlation between models it depends on many factors like type of problem type of data quality of models correlation of models expected result etc some example configurations are listed below reasonable starting point l1 2 10 models l2 weighted rank average or single model then try to add more 1st level models and additional level l1 10 50 models l2 2 10 models l3 weighted rank average if you re crunching numbers at kaggle and decided to go wild l1 100 inf models l2 10 50 models l3 2 10 models l4 weighted rank average you can also find some winning stacking architectures on kaggle blog http blog kaggle com e g 1st place in homesite quote conversion http blog kaggle com 2016 04 08 homesite quote conversion winners write up 1st place kazanova faron clobber 17 how many stacking levels should i use note 1 the best architecture can be found only by experiment note 2 always remember that higher number of levels or models does not guarantee better result the key to success in stacking and ensembling in general is diversity low correlation between models for some example configurations see q16 https github com vecxoz vecstack 16 how many models should i use on a given stacking level 18 how do i choose models for stacking based on experiments and correlation e g pearson less correlated models give better result it means that we should never judge our models by accuracy only we should also consider correlation how given model is different from others sometimes inaccurate but very different model can add substantial value to resulting ensemble 19 i am trying hard but still can t beat my best single model with stacking what is wrong nothing is wrong stacking is advanced complicated technique it s hard to make it work solution make sure to try weighted rank average first instead of additional level with some advanced models average is much easier to apply and in most cases it will surely outperform your best model if still no luck then probably your models are highly correlated 20 what should i choose functional api stacking function or scikit learn api stackingtransformer quick guide by default start from stackingtransformer with familiar scikit learn interface and logic if you need low ram consumption try stacking function but remember that it does not store models and does not have scikit learn capabilities stacking api comparison property stacking function stackingtransformer execution time same same ram consumes the smallest possible amount of ram does not store models at any point in time only one model is alive logic train model predict delete etc when execution ends all ram is released consumes much more ram it stores all models built in each fold this price is paid for standard scikit learn capabilities like pipeline and featureunion access to models after training no yes compatibility with pipeline and featureunion no yes estimator implementation restrictions must have only fit and predict predict proba methods must be fully scikit learn compatible nan and inf in input data allowed not allowed can automatically save oof and log in files yes no input dimensionality x train x test arbitrary 2 d 21 how do parameters of stacking function and stackingtransformer correspond stacking function stackingtransformer models ridge estimators ridge ridge mode oof pred bag alias a variant a mode oof pred alias b variant b 22 why scikit learn api was implemented as transformer and not predictor by nature stacking procedure is predictor but by application it is definitely transformer transformer architecture was chosen because first of all user needs direct access to oof i e the ability to compute correlations weighted average etc if you need predictor based on stackingtransformer you can easily create it via pipeline by adding on the top of stackingtransformer some regressor or classifier transformer makes it easy to create any number of stacking levels using pipeline we can easily create multilevel stacking by just adding several stackingtransformer s on top of each other and then some final regressor or classifier 23 how to estimate stacking training time and number of models which will be built note stacking usually takes long time it s expected inevitable behavior we can compute total number of models which will be built during stacking procedure using following formulas variant a n models total n estimators n folds variant b n models total n estimators n folds n estimators let s look at example say we define our stacking procedure as follows python estimators l1 lr linearregression ridge ridge stack stackingtransformer estimators l1 n folds 4 so we have two 1st level estimators and 4 folds it means stacking procedure will build the following number of models variant a 8 models total each model is trained on 3 4 of x train variant b 10 models total 8 models are trained on 3 4 of x train and 2 models on full x train compute time if estimators have relatively similar training time we can roughly compute total training time as time total n models total time of one model if estimators have different training time we should compute number of models and time for each estimator separately set n estimators 1 in formulas above and then sum up times 24 which stacking variant should i use a oof pred bag or b oof pred you can find out only by experiment default choice is variant a because it takes less time and there should be no significant difference in result but of course you may also try variant b for more details see stacking tutorial https github com vecxoz vecstack blob master examples 00 stacking concept pictures code ipynb 25 how to choose number of folds note remember that higher number of folds substantially increase training time and ram consumption for stackingtransformer see q23 https github com vecxoz vecstack 23 how to estimate stacking training time and number of models which will be built standard approach 4 or 5 folds if data is big 3 folds if data is small you can try more folds like 10 or so 26 when i transform train set i see train set was detected what does it mean note 1 it is not allowed to change train set between calls to fit and transform methods due to stacking nature transformation is different for train set and any other set if train set is changed after training stacking procedure will not be able to correctly identify it and transformation will be wrong note 2 to be correctly detected train set does not necessarily have to be identical exactly the same it must have the same shape and all values must be close np isclose is used for checking so if you somehow regenerate your train set you should not worry about numerical precision if you transform x train and see train set was detected everything is ok if you transform x train but you don t see this message then something went wrong probably your train set was changed it is not allowed in this case you have to retrain stackingtransformer for more details see stacking tutorial https github com vecxoz vecstack blob master examples 00 stacking concept pictures code ipynb or q8 https github com vecxoz vecstack 8 why do i need complicated inner procedure for stacking 27 how is the very first stacking level called l0 or l1 where does counting start common convention the very first bunch of models which are trained on initial raw data are called l1 on top of l1 we have so called stacker level or meta level or l2 i e models which are trained on predictions of l1 models count continues in the same fashion up to arbitrary number of levels i use this convention in my code and docs but of course your kaggle teammates may use some other naming approach so you should clarify this for your specific case 28 can i use randomized gridsearchcv to tune the whole stacking pipeline yes technically you can but it is not recommended because this approach will lead to redundant computations general practical advice is to tune each estimator separately and then use tuned estimators on the 1st level of stacking higher level estimators should be tuned in the same fashion using oof from previous level for manual tuning you can use stacking function or stackingtransformer with a single 1st level estimator 29 how to define custom metric especially auc python from sklearn metrics import roc auc score from sklearn preprocessing import onehotencoder def auc y true y pred roc auc metric for both binary and multiclass classification parameters y true 1d numpy array true class labels y pred 2d numpy array predicted probabilities for each class ohe onehotencoder sparse false y true ohe fit transform y true reshape 1 1 auc score roc auc score y true y pred return auc score 30 do folds splits have to be the same across estimators and stacking levels how does random state work to ensure better result folds splits have to be the same across all estimators and all stacking levels it means that random state has to be the same in every call to stacking function or stackingtransformer this is default behavior of stacking function and stackingtransformer by default random state 0 if you want to try different folds splits try to set different random state values stacking concept 1 we want to predict train set and test set with some 1st level model s and then use these predictions as features for 2nd level model s 2 any model can be used as 1st level model or 2nd level model 3 to avoid overfitting for train set we use cross validation technique and in each fold we predict out of fold oof part of train set 4 the common practice is to use from 3 to 10 folds 5 predict test set variant a in each fold we predict test set so after completion of all folds we need to find mean mode of all temporary test set predictions made in each fold variant b we do not predict test set during cross validation cycle after completion of all folds we perform additional step fit model on full train set and predict test set once this approach takes more time because we need to perform one additional fitting 6 as an example we look at stacking implemented with single 1st level model and 3 fold cross validation 7 pictures variant a three pictures describe three folds of cross validation after completion of all three folds we get single train feature and single test feature to use with 2nd level model variant b first three pictures describe three folds of cross validation like in variant a to get single train feature and fourth picture describes additional step to get single test feature 8 we can repeat this cycle using other 1st level models to get more features for 2nd level model 9 you can also look at animation of variant a https github com vecxoz vecstack variant a animation and variant b https github com vecxoz vecstack variant b animation variant a fold 1 of 3 https github com vecxoz vecstack raw master pic dia1 png fold 1 of 3 fold 2 of 3 https github com vecxoz vecstack raw master pic dia2 png fold 2 of 3 fold 3 of 3 https github com vecxoz vecstack raw master pic dia3 png fold 3 of 3 variant a animation variant a animation https github com vecxoz vecstack raw master pic animation1 gif variant a animation variant b step 1 of 4 https github com vecxoz vecstack raw master pic dia4 png step 1 of 4 step 2 of 4 https github com vecxoz vecstack raw master pic dia5 png step 2 of 4 step 3 of 4 https github com vecxoz vecstack raw master pic dia6 png step 3 of 4 step 4 of 4 https github com vecxoz vecstack raw master pic dia7 png step 4 of 4 variant b animation variant b animation https github com vecxoz vecstack raw master pic animation2 gif variant b animation | stacking stacked-generalization explain-stacking stacking-tutorial blending bagging ensembling ensemble ensemble-learning machine-learning | ai |
eng-exercise-data | eng exercise data engineering exercises around data modeling databases storage of all kinds glossary of terms this glossary https raima com database terminology was a starting point for many of these definitions relational database a database in which inter table relationships are organized primarily through common data columns which define a one to many relationship between a row of the primary key table and one or more rows of the matching foreign key table joins relate tables that have matching primary foreign key values but other comparisons relationships may be defined in addition to describing how the database tables are related the relational model also defines how the related data can be accessed and manipulated sql is the most commonly used relational model database language table a collection of logically related columns a table consists of rows each of which shares the same columns but vary in the column values column a single unit of named data that has a particular data type e g number text or date columns only exist in tables data type the basic kind of data that can be stored in a column the data types that are available in sql are char wchar varchar wvarchar binary varbinary boolean tinyint smallint integer bigint real float double date time timestamp long varbinary long varchar and long wvarchar sqlite has a set https www sqlite org datatype3 html of defined types and postgres a type of sql used often at truss offers lots of types https www postgresql org docs 9 5 datatype html primary key a column or group of columns in a given table that uniquely identifies each row of the table the primary key is used in conjunction with a foreign key in another or even the same table to relate the two tables together for example the primary key in an author table would match the foreign key in a book table in order to relate a particular author to that author s books constraints constraints are the rules enforced on the data columns of a table these are used to limit the kind of data that can go into a table this ensures the accuracy and reliability of the data in the database read more about constraints here https www tutorialspoint com sql sql constraints htm foreign key one or more columns in a table intended to contain only values that match the related primary unique key column s in the referenced table foreign and primary keys explicitly define the direct relationships between tables referential integrity is maintained when every foreign key refers to one and only one existing primary key index a separate structure that allows fast access to a table s rows based on the data values of the columns used in the index migration at a pure database level this typically means moving your data from one platform to another ie moving from physical hardware to a cloud platform or one cloud service to another as a developer a database migration more commonly refers to a way of tracking data schema changes under source control using a tool that implements those incremental changes as they happen one example of a migration tool is flyway https flywaydb org documentation command migrate object relational mapping orm object relational mapping orm o rm and o r mapping tool is a programming technique for converting data between incompatible type systems using object oriented programming languages this creates in effect a virtual object database that can be used from within the programming language a popular example is active record https guides rubyonrails org association basics html which comes built in with ruby on rails | server |
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emacsql | emacsql emacsql is a high level emacs lisp front end for sqlite primarily postgresql mysql and potentially other sql databases any readable lisp value readable can be stored as a value in emacsql including numbers strings symbols lists vectors and closures emacsql has no concept of text values it s all just lisp objects the lisp object nil corresponds 1 1 with null in the database on melpa each back end is provided as a separate package suffixed with the database name in the case of emacsql sqlite on first use emacsql will attempt to find a c compiler and use it to compile a custom native binary for communicating with a sqlite database requires emacs 25 or later faq why are all values stored as strings emacsql is not intended to interact with arbitrary databases but to be an acid compliant database for emacs extensions this means that emacsql cannot be used with a regular sql database used by other non emacs clients all database values must be s expressions when emacsql stores a value string symbol cons etc it is printed and written to the database in its printed form strings are wrapped in quotes and escaped as necessary that means bare symbols in the database generally look like strings the only exception is nil which is stored as null will emacsql ever support arbitrary databases the author of emacsql thinks mistake that it was probably a design mistake to restrict it to emacs by storing only printed values and that it would be a lot more useful if it just handled primitive database types however emacsql is in maintenance mode and there are no plans to make any fundamental changes not least because they would break all existing packages and databases that rely on the current emacsql behavior windows issues emacs start process shell command function is not supported on windows since both emacsql mysql and emacsql psql rely on this function neither of these connection types are supported on windows example usage el defvar db emacsql sqlite company db create a table table and column identifiers are symbols emacsql db create table people name id salary or optionally provide column constraints emacsql db create table people name id integer primary key salary float insert some data emacsql db insert into people values jeff 1000 60000 0 susan 1001 64000 0 query the database for results emacsql db select name id from people where salary 62000 susan 1001 queries can be templates using 1 2 etc emacsql db select name id from people where salary s1 50000 jeff 1000 susan 1001 when editing these prepared sql s expression statements the m x emacsql show last sql command think eval last sexp is useful for seeing what the actual sql expression will become when compiled schema a table schema is a list whose first element is a vector of column specifications the rest of the list specifies table constraints a column identifier is a symbol and a column s specification can either be just this symbol or it can include constraints as a list because emacsql stores entire lisp objects as values the only relevant and allowed types are integer float and object default column table constraint dashes in identifiers are converted into underscores when compiled into sql this allows for lisp style identifiers to be used in sql constraints follow the compilation rules below el no constraints schema with four columns name id building room add some column constraints name unique id integer primary key building room add some table constraints name unique id integer primary key building room unique building room check id 0 here s an example using foreign keys el subjects table schema id integer primary key subject tag table references subjects subject id integer tag foreign key subject id references subjects id on delete cascade foreign key constraints are enabled by default in emacsql operators expressions are written lisp style with the operator first if it looks like an operator emacsql treats it like an operator however several operators are special funcall quote the and operators accept 2 or 3 operands transforming into a sql between and operator as appropriate for function like operators like count and max use the funcall operator el select funcall max age from people inside expressions emacsql cannot tell the difference between symbol literals and column references if you re talking about the symbol itself just quote it as you would in normal elisp note that this does not escape tn parameter symbols el emacsql db where category hiking quoting a string makes emacsql handle it as a raw string these raw strings are not printed when being assembled into a query these are intended for use in special circumstances like filenames attach or pattern matching like it is vital that raw strings are not returned as results el emacsql db where like name foo emacsql db attach path to foo db as foo since template parameters include their type they never need to be quoted with glob and like sql operators keep in mind that they re matching the printed representations of these values even if the value is a string the concatenation operator is unsupported because concatenating printed representations breaks an important constraint all values must remain readable within sqlite prepared statements the database is interacted with via prepared sql s expression statements you shouldn t normally be concatenating strings on your own and it leaves out any possibility of a sql injection see the usage section above for examples a statement is a vector of keywords and other lisp object prepared emacsql s expression statements are compiled into sql statements the statement compiler is memorized so that using the same statement multiple times is fast to assist in this the statement can act as a template using i1 s2 etc working like the elisp format function compilation rules rather than the typical uppercase sql keywords keywords in a prepared emacsql statement are literally just that lisp keywords emacsql only understands a very small amount of sql s syntax the compiler follows some simple rules to convert an s expression into sql all prepared statements are vectors a prepared s expression statement is a vector beginning with a keyword followed by a series of keywords and special values this includes most kinds of sub queries el select from select tag from tags where in tag select keywords are split and capitalized dashes are converted into spaces and the keyword gets capitalized for example if not exists becomes if not exists how you choose to combine keywords is up to your personal taste e g drop table vs drop table standalone symbols are identifiers emacsql doesn t know what symbols refer to identifiers and what symbols should be treated as values use quotes to mark a symbol as a value for example people here will be treated as an identifier el insert into people values row oriented information is always represented as vectors this includes rows being inserted and sets of columns in a query if you re talking about a row like thing then put it in a vector el select id name from people note that is actually a sql keyword so don t put it in a vector el select from lists are treated as expressions this is true even within row oriented vectors el where name bob select seconds 60 count from some things that are traditionally keywords particularly those that are mixed in with expressions have been converted into operators as asc desc el order by asc b desc a order by b asc a desc select p name from as people p select p name from people as p the values keyword is special what follows values is always treated like a vector or list of vectors normally this sort of thing would appear to be a column reference el values 1 2 3 values 1 2 3 4 5 6 insert multiple rows a list whose first element is a vector is a table schema this is to distinguish schemata from everything else with the exception of what follows values nothing else is shaped like this el create table people id primary key name templates to make statement compilation faster and to avoid making you build up statements dynamically you can insert tn parameters in place of identifiers and values these refer to the argument s type and its argument position after the statement in the emacsql function one indexed el emacsql db select from i1 where salary s2 employees 50000 emacsql db select from employees where like name r1 smith the letter before the number is the type i identifier s scalar v vector or multiple vectors r raw unprinted strings s schema when combined with values the vector type can refer to lists of rows el emacsql db insert into favorite characters values v1 0 calvin 1 hobbes 3 susie this is why rows must be vectors and not lists sqlite support the custom emacsql sqlite binary is compiled with soundex soundex and full text search fts fts3 fts4 and fts5 enabled features disabled by the default sqlite build this back end should work on any system with a conforming ansi c compiler installed under a command name listed in emacsql sqlite c compilers ignored features emacsql doesn t cover all of sqlite s features here are a list of things that aren t supported and probably will never be collating sqlite has three built in collation functions binary default nocase and rtrim emacsql values never have right hand whitespace so rtrim won t be of any use nocase is broken ascii only and there s little reason to use it text manipulation functions like collating this is incompatible with emacsql s expression storage date and time these are incompatible with the printed values stored by emacsql and therefore have little use limitations emacsql is not intended to play well with other programs accessing the sqlite database non numeric values are stored encoded as s expressions text values this avoids ambiguities in parsing output from the command line and allows for storage of emacs richer data types this is an efficient acid compliant database specifically for emacs emacs lisp indentation annoyance by default emacs lisp mode indents vectors as if they were regular function calls el ugly indentation emacsql db select from people where age 60 calling the function emacsql fix vector indentation interactive advises the major mode to fix this annoyance el such indent emacsql db select from people where age 60 contributing and extending to run the test suite clone the pg and sqlite3 packages into sibling directories the makefile will automatically put these paths on the emacs load path override ldflags if your situation is different shell git clone https github com emarsden pg el pg git clone https github com pekingduck emacs sqlite3 api sqlite3 or set load path to point at these packages elsewhere shell make load path l path to pg l path to sqlite3 then invoke make shell make test if the environment variable pgdatabase is present then the unit tests will also be run with postgresql emacsql psql provide pghost pgport and pguser if needed if pguser is provided the pg el back end emacsql pg will also be tested if the environment variable mysql dbname is present then the unit tests will also be run with mysql in the named database note that this is not an official mysql variable just something made up for emacsql creating a new front end emacsql uses eieio so that interactions with a connection occur through generic functions you need to define a new class that inherits from emacsql connection implement emacsql send message emacsql waiting p emacsql parse and emacsql close provide a constructor that initializes the connection and calls emacsql register for automatic connection cleanup provide emacsql types if needed hint use a class allocated slot ensure that you properly read null as nil hint ask your back end to print it that way register all reserved words with emacsql register reserved preferably provide emacsql reconnect if possible set the default isolation level to serializable enable autocommit mode by default prefer ansi syntax value escapes identifier escapes etc enable foreign key constraints by default the goal of the autocommit isolation parsing and foreign key configuration settings is to normalize the interface as much as possible the connection s user should have the option to be agnostic about which back end is actually in use the provided implementations should serve as useful examples if your back end outputs data in a clean standard way you may be able to use the emacsql protocol mixin class to do most of the work see also sqlite documentation https www sqlite org docs html br br compile https github com magit emacsql actions workflows compile yml badge svg https github com magit emacsql actions workflows compile yml test https github com magit emacsql actions workflows test yml badge svg https github com magit emacsql actions workflows test yml nongnu elpa https elpa nongnu org nongnu emacsql svg https elpa nongnu org nongnu emacsql html melpa stable https stable melpa org packages emacsql badge svg https stable melpa org emacsql melpa https melpa org packages emacsql badge svg https melpa org emacsql readable http nullprogram com blog 2013 12 30 almost everything prints readably fts http www sqlite org fts3 html soundex http www sqlite org compile html soundex mistake https github com magit emacsql issues 35 issuecomment 346352439 localwords eieio elisp emacsql fts melpa makefile nocase rtrim sql s soundex localwords autocommit el emacsql mixin psql schemas unprinted whitespace | front_end |
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NakedTensor | naked tensor this is a bare bones example of tensorflow a machine learning package published by google you will not find a simpler introduction to it in each example a straight line is fit to some data values for the slope and y intercept of the line that best fit the data are determined using gradient descent if you do not know about gradient descent check out the wikipedia page https en wikipedia org wiki gradient descent alt text artwork line of best fit jpg straight line fitted to data after creating the required variables the error between the data and the line is defined the definition of the error is plugged into the optimizer tensorflow is then started and the optimizer is repeatedly called this iteratively fits the line to the data by minimizing the error read the scripts in this order serial py tensor py bigdata py serial py the purpose of this script is to illustrate the nuts and bolts of a tensorflow model the script makes it easy to understand how the model is put together the error between the data and the line is defined using a for loop because of the way the error is defined the calculation runs in serial tensor py this script goes a step farther than serial py although it actually requires fewer lines of code the outline of the code is the same as before except this time the error is defined using tensor operations because tensors are used the code can run in parallel you see each point of data is treated as being independent and identically sampled because each point of data is assumed to be independent the calculations are too when you use tensors each point of data is run on separate computing cores there are 8 points of data so if you have a computer with eight cores it should run almost eight times faster bigdata py you are one buzzword away from being a professional instead of fitting a line to just eight datapoints we will now fit a line to 8 million datapoints welcome to big data there are two major changes in the code the first is bookkeeping because of all the data the error must be defined using placeholders instead of actual data later in the code the data is fed through the placeholders the second change is that because we have so much data only a sample of data is fed into the model at any given time each time an operation of gradient descent is called a new sample of data is fed into the model by sampling the dataset tensorflow never has to deal with the entire dataset at once this works surprisingly well and there is theory https en wikipedia org wiki stochastic gradient descent that says it is okay to do this there are a few conditions that the theory says are important like the step size must decrease with each iteration for now who cares it works conclusion as you worked through the scripts you hopefully saw how the error can be anything you wanted it to be it could be the error between a set of images and a convolutional neural network it could be the error between classical music and a recurrent neural network let your imagination run wild once the error is defined you can use tensorflow to try and minimize it that s it hopefully you found this tutorial enlightening requirements python3 https www python org tensorflow https www tensorflow org numpy http www numpy org | tensorflow tensorflow-tutorials distributed-computing simple big-data linear-regression tensorflow-examples tensorflow-exercises | ai |
iqbalhasandev | br br a href https iqbalhasan dev traget blank center span style background white img title iqbalhasan dev src image logo png alt iqbal hasan style background white span br br views https api iqbalhasan dev api visitor count and svg center a hey there i am iqbal hasan https iqbalhasan dev self taught laravel developer sr web app developer from bangladesh i am specialized in backend development my mission is to convert an idea into a real pixel perfect application i love to talk on trending technology i have huge attraction on linux and i am a bit mad when i code want to know more about me check out my portfolio https iqbalhasan dev currently i am focusing on laravel https laravel com and server management goal of 2023 is to learn optimize code vue js industrial good practices br about me hr br i am iqbal hasan https iqbalhasan dev i am a student studying bsc in computer science engineering i love working on various technologies in addition to my studies i love coding 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Draw-a-iottery | draw a iottery gif https github com miumiu s draw a iottery blob master 3888312 66a666dee47ff8bd gif | server |
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Pruning-and-Refactoring-Tool | pruning and refactoring tool running the pruning and refactoring tool requires the same environment as the uml yang mapping tool nodejs and its xmlreader module the user can refer to the user guide https github com opennetworkingfoundation eagle open model profile and tools blob umlyangtools uml yang 20mapping 20tool 20user 20guide v1 3 0601 docx an introduction video on setting up the environment is on youtube https www youtube com watch v 6at3yfre8ag feature youtu be after the environment is configured the user can do the following to run the tool step 1 copy the source information model which is ready for pruning and refactoring to the project folder step 2 change the file name of the information model to source uml step 3 type the following command in the project directory in terminal node main js when the user runs the tool for the first time target uml and mapping uml are generated in the project folder the target uml is a clone of the source uml and the mapping uml file is composed of the pruning and refactoring realizations between the source and the target model the mapping model is independent of the source model and the target model the elements in all three models can be dragged onto papyrus diagrams if the source uml and target uml are both existed before running the tool the mapping uml is updated to reflect the differences between classes attributes and associations in the source and the target model the comparison results are nested as comments inside the specific pruning and refactoring realization future work 1 detailed comparison results between the source and the target model will be included in the mapping model 2 class split function the refactoring function is under development which will allow the user to copy or split a object class in the target model the class copy function is completed the user will need to feed the tool with the class name and the number of copies or splits following a certain format in a text file 3 reverse pruning function the reverse pruning function will allow the user to add the non experimental elements in the target model back to the source model | server |
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calypso_os | this operating system is designed to run on low power embedded devices it has a pre emptive scheduling algorithm and offers a posix like interface to the apps building download the sources with git clone and update the submodules for this project git clone https github com sebastianene07 calypso os git git submodule update init recursive example for building calypso os for norfic nrf5x board make config machine type nrf5x nrf52840 make j or if we have nrf52832 make config machine type nrf5x nrf52832 make j you can enable different features for your board by using the kconfig menu selection make menuconfig the above command requires you to have kconfig mconf installed on your build machine current features 1 task scheduling and basic synchronization primitives the os defines two list g tcb waiting list is holding the tasks in one of the following states nbsp ready waiting for sem halt nbsp g tcb list is holding the tasks in the ready and running state nbsp the g current tcb pointer points to the current running task nbsp a task transitions from running to waiting for semaphore nbsp when it tries to acquire a semaphore that has a value 0 nbsp the task is placed in the g tcb waiting list and it s context nbsp is saved on the stack nbsp to wakeup a task from the waiting for semaphore state nbsp someone has to call sem post on the semaphore nbsp that keeps the task from running once this semaphore nbsp is posted the task enters the ready state and when the nbsp idle task runs it moves the task from the g tcb waiting list nbsp to g tcb list list nbsp 2 dynamic memory allocation the allocator is implemented as part of a submodule in s alloc it can be configure to run with multipple heap objects depending on the needs it supports the following operations s alloc s free s realloc and the code is tested in a sample program main c that verify the integrity of the allocated blocks and monitors the fragmentation the test also verifies that the allocated regions do not overlap in the current configuration it supports up to 2 31 1 blocks of memory where the block size is defined by the code that it s using the allocator the allocation is done in o n space time searching through the list of free nodes whereas the free operation can take o n 2 because of the block merging and sorting 3 virtual file system the virtual file system contains a tree like structure with nodes that allows us to interract with the system resources the current nodes are root node dev mnt bin otp home ttyusb0 rtc0 spi0 a task contains a list of struct opened resource s which is essentially the struct file from linux and has the same role the registered nodes in the virtual file sytem are described by struct vfs node s and these keep informations such as device type supported operations name private data the open device flow open vfs get matching node extract the node from the vfs and call into ops open cb sched allocate resource allocate a new opened resource s in the calling process return the index of the opened resource s structure as the file descriptor vfs node open call the appropriate node open function todo s the project has a board associated to track issues request new features https github com users sebastianene07 projects 1 card 30262282 1 virtual file system support 1 1 add support for fatfs http elm chan org fsw ff 00index e html done 1 1 1 implement sd mmc driver done 1 1 2 add mtd layer done 1 2 add functional tests for memory allocator done 1 3 add functional tests for the simulator which run in ci done 2 refactorization driver lower half upper half separation done 3 tickless kernel done 4 application support and basic shell done 5 ble support with nrf5x family and softdevice done porting guide run the script to add a new board user calypso os add board sh do you wish to create a new board y y n y yes what name has the board awesome board confirm board name awesome board y y n y what arch is the board arm sim template xtensa x86 arch x86 is new confirm adding it y y n y creating board in arch x86 awesome board creating config entry in config awesome board done after this there are a bunch of filed generated from the template this files contains sources makefiles and kconfig options a good place to start is to edit the memory layout in the config board name scripts linker ld the next step is to specify the cross compiler prefix from the defconfig check out the board interface defined here https github com sebastianene07 calypso os tree master config to implement the minimum support define and use the functions marked as mandatory contributions contributions are welcome you can email me on sebastian ene sebastian ene07 gmail com | embedded-devices memory-allocator rtos scheduler | os |
RTOS | rtos free rtos stm32f103c8 peripheral uart | os |
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stat-nlp-fall2017 | stat nlp fall2017 homework assignments for statistical natural language processing new york university fall 2017 | natural-language-processing statistical-learning machine-learning deep-learning | ai |
Ring-Buffer | ring buffer a simple ring buffer circular buffer designed for embedded systems an example is given in examples simple c examples simple c the size of the memory buffer must be a power of two and the ring buffer can contain at most buf size 1 bytes a new ring buffer is created using the ring buffer init ring buffer buff sizeof buff function c char buff 64 ring buffer t ring buffer ring buffer init ring buffer buff sizeof buff in this case the buffer size is 64 bytes and the ring buffer can contain 63 bytes the module provides the following functions for accessing the ring buffer documentation can be found in ringbuffer h ringbuffer h c void ring buffer queue ring buffer t buffer char data void ring buffer queue arr ring buffer t buffer const char data ring buffer size t size uint8 t ring buffer dequeue ring buffer t buffer char data ring buffer size t ring buffer dequeue arr ring buffer t buffer char data ring buffer size t len uint8 t ring buffer peek ring buffer t buffer char data ring buffer size t index uint8 t ring buffer is empty ring buffer t buffer uint8 t ring buffer is full ring buffer t buffer ring buffer size t ring buffer num items ring buffer t buffer | c circular-buffer ring-buffer buffer ringbuffer circularbuffer | os |
389Kspring18 | cmsc389k full stack web development with node js imgur http i imgur com 19j0ajp png course description this course provides a comprehensive practical introduction to modern full stack web development using javascript and node js the course will start with basic html css javascript then we will move into node js and learn how to deploy a website from there we will learn about express js server side development module and mongodb database in order to create a complete web application course details course cmsc389k https ntst umd edu soc search courseid cmsc389k sectionid termid 201801 opensectionsonly on creditcompare credits courselevelfilter all instructor facetoface on blended on online on coursestartcompare coursestarthour coursestartmin coursestartam courseendhour courseendmin courseendam teachingcenter all classday1 on classday2 on classday3 on classday4 on classday5 on prerequisites c or better in cmsc216 and cmsc250 credits 1 seats 30 lecture time fridays 1 1 50pm location csi 2118 semester spring 2018 textbook none course facilitators timothy chen https www linkedin com in timothychen01 and allen cheng https www linkedin com in allen cheng 6a40388a faculty advisor john dickerson http jpdickerson com syllabus last updated january 19 2018 topics covered html and css javascript ecmascript 2015 e6 variables data types expressions operators conditionals iteration statements functions functional programming callbacks interaction with dom dom manipulation event handling jquery ajax node js file manipulation modules server applications writing server applications in node js and express mongodb deploying and hosting server applications grading grades will be maintained on the cs department grades server https grades cs umd edu you are responsible for all material discussed in lecture and posted on the class repository including announcements deadlines policies etc your final course grade will be determined according to the following percentages percentage title description 55 projects weekly individual projects that teach practical skills and real life applications 20 midterm examination 25 final project final project to demonstrate mastery of all topics learned and apply knowledge to create a new application from scratch any request for reconsideration of any grading on coursework must be submitted within one week of when it is returned no requests will be considered afterwards week topic assignment 1 1 26 html css personal website p1 2 2 2 javascript language w dom p1 due js function implementation p2 3 2 9 jquery and ajax p2 due 50 state game p3 4 2 16 node js 5 2 23 express js p3 due pok mon api p4 6 3 2 express js cont 7 3 9 express js cont p4 due 8 3 16 midterm representatives website p5 break 3 23 final project 9 3 30 databases p5 due bitcamp 4 6 hosting git 11 4 13 mega boilerplate 12 4 20 web sockets 13 4 27 deploying via now 14 5 4 presentations final project due projects projects must be submitted electronically following the instructions given in each project assignment projects may not be submitted by any other means e g please do not email your projects to us it is your responsibility to test your program and verify that it works properly before submitting all projects are due at 11 59 pm on the day indicated on the project assignment projects may be submitted up to 24 hours late for a 10 penalty if you submit both on time late your project will receive the maximum of the penalty adjusted scores you may submit multiple times unlike lower level programming classes we will not provide you with test cases e g public tests before projects are due you will be responsible for developing your own tests and for using appropriate testing techniques also we expect your projects to use proper style and documentation outside of class communication with course staff we will interact with students outside of class in primarily two ways online at piazza piazza com umd spring2018 cmsc389k home and office hours by appointment email should only be used for emergencies and non class related questions e g projects instructor dr john dickerson http jpdickerson com john cs umd edu tas timothy chen http timothychen me tchen112 terpmail umd edu allen cheng http allencheng me ac allencheng me excused absence and academic accommodations see the section titled attendance absences or missed assignments available at course related policies http www ugst umd edu courserelatedpolicies html disability support accommodations see the section titled accessibility available at course related policies http www ugst umd edu courserelatedpolicies html academic integrity note that academic dishonesty includes not only cheating fabrication and plagiarism but also includes helping other students commit acts of academic dishonesty by allowing them to obtain copies of your work in short all submitted work must be your own cases of academic dishonesty will be pursued to the fullest extent possible as stipulated by the office of student conduct http osc umd edu osc default aspx it is very important for you to be aware of the consequences of cheating fabrication facilitation and plagiarism for more information on the code of academic integrity or the student honor council please visit http www shc umd edu course evaluations if you have a suggestion for improving this class don t hesitate to tell the instructor or tas during the semester at the end of the semester please don t forget to provide your feedback using the campus wide courseevalum system your comments will help make this class better thanks to the writers of this https www cs umd edu class fall2016 cmsc330 syllabus shtml syllabus for the wording of much of this document and to ishaan parikh https www linkedin com in iparikh and sashank thupukari https www linkedin com in sthupukari for creating this course and the stics program | front_end |
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fklearn | fklearn functional machine learning pypi https img shields io pypi v fklearn svg style flat square documentation status https readthedocs org projects fklearn badge version latest https fklearn readthedocs io en latest badge latest gitter https badges gitter im fklearn python community svg https gitter im fklearn python community utm source badge utm medium badge utm campaign pr badge tests https github com nubank fklearn actions workflows push yaml badge svg branch master license https img shields io badge license apache 202 0 blue svg https opensource org licenses apache 2 0 fklearn uses functional programming principles to make it easier to solve real problems with machine learning the name is a reference to the widely known scikit learn https scikit learn org stable library fklearn principles 1 validation should reflect real life situations 2 production models should match validated models 3 models should be production ready with few extra steps 4 reproducibility and in depth analysis of model results should be easy to achieve documentation https fklearn readthedocs io en latest getting started https fklearn readthedocs io en latest getting started html api docs https fklearn readthedocs io en latest api modules html contributing https fklearn readthedocs io en latest contributing html installation to install via pip pip install fklearn you can also install from the source sh git clone git github com nubank fklearn git cd fklearn git checkout master pip install e license apache license 2 0 license | data-science machine-learning python data-analysis ml | ai |
rtos_ground_up | build your own realtime os rtos from the ground up on arm this repository contains my individual work for the build your own realtime os rtos from ground up on arm 1 https www udemy com course rtos building from ground up on arm processors udemy class created and taught by israel gbati cortex png cortex png overview the objective of this online class is the build your own realtime operating systems from first principles on a stm32 discovery board from the course description this course teaches you how to build a real time operating systems through intensive practice and theory it starts by getting you excited through an introduction to the internals of a real time kernel on arm processors which you shall implement yourself in code then we move on to learn all there is about real time operating systems their various parts how they work and then we finally build our own real time operating system exploring different scheduling algorithms and inter thread communication tools at the end of this course you should be able to build your own real time operating system from scratch give your own lecture on real time operating systems be able to build a round robin scheduler be able to build a periodic scheduler be able to calculate the cpu utilization of your rtos be able to build an os kernel etc please see the course curriculum section to find out all the amazing content awaiting you concepts and skills learned during the course include build a real time operating system from scratch build round robin schedulers build cooperative schedulers build periodic schedulers build first come first served scheduler build rate monotonic schedulers build a board support package from scratch calculate the cpu utilization of an rtos write bare metal embedded c code write assembly code understand the os support features of the cortex m architecture understand the internals of an rtos kernel be implement to implement and explain popular scheduling algorithms be able to explain the cortex m architecture be able to give a lecture on real time this repository content contained within this repository is my own work produced as a result of completing this course work being completed projects deliverables and notes lectures and course material are not included however | rtos c bare-metal assembly arm stm32f4-discovery | os |
MANISH-UPADHYAY | manish upadhyay information amp technology | server |
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3d-vision-transformers | this repo supplements our 3d vision with transformers survey https arxiv org abs 2208 04309 jean lahoud jiale cao fahad shahbaz khan hisham cholakkal rao muhammad anwer salman khan ming hsuan yang this repo includes all the 3d computer vision papers with transformers which are presented in our paper https arxiv org abs 2208 04309 and we aim to frequently update the latest relevant papers p align center img src https user images githubusercontent com 14073587 183882596 ada49e17 bbd5 4b09 962b e0ff1d8291c0 png width 600 p content object classification object classification br 3d object detection 3d object detection br 3d segmentation 3d segmentation br complete scenes segmentation complete scenes segmentation br point cloud video segmentation point cloud video segmentation br medical imaging segmentation medical imaging segmentation br 3d point cloud completion 3d point cloud completion br 3d pose estimation 3d pose estimation br other tasks other tasks br 3d tracking 3d tracking br 3d motion prediction 3d motion prediction br 3d reconstruction 3d reconstruction br point cloud registration point cloud registration br object classification group in group relation based transformer for 3d point cloud learning rs 2022 pdf https www mdpi com 2072 4292 14 7 1563 pdf version 1648109597 br masked autoencoders for point cloud self supervised learning eccv 2022 pdf https arxiv org pdf 2203 06604 code https github com pang yatian point mae br 3dctn 3d convolution transformer network for point cloud classification t its 2022 pdf https arxiv org pdf 2203 00828 br lft net local feature transformer network for point clouds analysis t its 2022 pdf https ieeexplore ieee org document 9700748 br sewer defect detection from 3d point clouds using a transformer based deep learning model automation in construction 2022 pdf https www mdpi com 1424 8220 22 12 4517 pdf version 1655277701 br 3d medical point transformer introducing convolution to attention networks for medical point cloud analysis arxiv 2021 pdf https arxiv org pdf 2112 04863 code https github com crane papercode 3dmedpt br point bert pre training 3d point cloud transformers with masked point modeling cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers yu point bert pre training 3d point cloud transformers with masked point modeling cvpr 2022 paper pdf code https github com lulutang0608 point bert br cpt convolutional point transformer for 3d point cloud processing accvw 2022 pdf https arxiv org pdf 2111 10866 br patchformer an efficient point transformer with patch attention cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers zhang patchformer an efficient point transformer with patch attention cvpr 2022 paper pdf br pvt point voxel transformer for point cloud learning arxiv 2021 pdf https arxiv org pdf 2108 06076 pdf code https github com haochengwan pvt br adaptive wavelet transformer network for 3d shape representation learning iclr 2021 pdf https openreview net pdf id 5mlb3clcjy br point cloud learning with transformer arxiv 2021 pdf https arxiv org pdf 2104 13636 br 3crossnet cross level cross scale cross attention network for point cloud representation ra l 2022 pdf https arxiv org pdf 2104 13053 br dual transformer for point cloud analysis ieee trans multimedia pdf https arxiv org pdf 2104 13044 br centroid transformers learning to abstract with attention arxiv 2021 pdf https arxiv org pdf 2102 08606 br pct point cloud transformer cvpr 2019 pdf http openaccess thecvf com content cvpr 2019 papers yang modeling point clouds with self attention and gumbel subset sampling cvpr 2019 paper pdf code https github com menghaoguo pct br point transformer iccv 2021 pdf https openaccess thecvf com content iccv2021 papers zhao point transformer iccv 2021 paper pdf code https github com postech cvlab point transformer br point transformer ieee access 2021 pdf https arxiv org pdf 2011 00931 code https github com engelnico point transformer br modeling point clouds with self attention and gumbel subset sampling cvpr 2019 pdf https openaccess thecvf com content cvpr 2019 papers yang modeling point clouds with self attention and gumbel subset sampling cvpr 2019 paper pdf br attentional shapecontextnet for point cloud recognition cvpr 2018 pdf http openaccess thecvf com content cvpr 2018 papers xie attentional shapecontextnet for cvpr 2018 paper pdf code https github com umyta a scn br 3d object detection bridged transformer for vision and point cloud 3d object detection cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers wang bridged transformer for vision and point cloud 3d object detection cvpr 2022 paper pdf br multimodal token fusion for vision transformers cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers wang multimodal token fusion for vision transformers cvpr 2022 paper pdf code https github com yikaiw tokenfusion br cat det contrastively augmented transformer for multi modal 3d object detection cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers zhang cat det contrastively augmented transformer for multi modal 3d object detection cvpr 2022 paper pdf br focused decoding enables 3d anatomical detection by transformers arxiv 2022 pdf https arxiv org pdf 2207 10774 pdf code https github com bwittmann transoar br monodetr depth aware transformer for monocular 3d object detection arxiv 2022 pdf https arxiv org pdf 2203 13310 code https github com zrrskywalker monodetr br transfusion robust lidar camera fusion for 3d object detection with transformers cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers bai transfusion robust lidar camera fusion for 3d object detection with transformers cvpr 2022 paper pdf code https github com xuyangbai transfusion br voxel set transformer a set to set approach to 3d object detection from point clouds cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers he voxel set transformer a set to set approach to 3d object detection cvpr 2022 paper pdf code https github com skyhehe123 voxset br vista boosting 3d object detection via dual cross view spatial attention cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers deng vista boosting 3d object detection via dual cross view spatial attention cvpr 2022 paper pdf code https github com gorilla lab scut vista br point density aware voxels for lidar 3d object detection cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers hu point density aware voxels for lidar 3d object detection cvpr 2022 paper pdf code https github com trailab pdv br petr position embedding transformation for multi view 3d object detection eccv 2022 pdf https www ecva net papers eccv 2022 papers eccv papers 136870523 pdf code https github com megvii research petr br arm3d attention based relation module for indoor 3d object detection comput vis pdf https link springer com content pdf 10 1007 s41095 021 0252 6 pdf code https github com lanlan96 arm3d br monodtr monocular 3d object detection with depth aware transformer cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers huang monodtr monocular 3d object detection with depth aware transformer cvpr 2022 paper pdf code https github com kuanchihhuang monodtr br attention based proposals refinement for 3d object detection iv 2022 pdf https arxiv org pdf 2201 07070 code https github com quan dao apro3d net br embracing single stride 3d object detector with sparse transformer cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers fan embracing single stride 3d object detector with sparse transformer cvpr 2022 paper pdf code https github com tusen ai sst br fast point transformer cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers park fast point transformer cvpr 2022 paper pdf code https github com postech cvlab fastpointtransformer br boxer box attention for 2d and 3d transformers cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers nguyen boxer box attention for 2d and 3d transformers cvpr 2022 paper pdf code https github com kienduynguyen boxer br detr3d 3d object detection from multi view images via 3d to 2d queries corl 2022 pdf https proceedings mlr press v164 wang22b wang22b pdf code https github com wangyueft detr3d br an end to end transformer model for 3d object detection iccv 2021 pdf http openaccess thecvf com content iccv2021 papers misra an end to end transformer model for 3d object detection iccv 2021 paper pdf code https github com facebookresearch 3detr br voxel transformer for 3d object detection iccv 2021 pdf http openaccess thecvf com content iccv2021 papers mao voxel transformer for 3d object detection iccv 2021 paper pdf code https github com pointscoder votr br improving 3d object detection with channel wise transformer iccv 2021 pdf http openaccess thecvf com content iccv2021 papers sheng improving 3d object detection with channel wise transformer iccv 2021 paper pdf code https github com hlsheng1 ct3d br m3detr multi representation multi scale mutual relation 3d object detection with transformers wacv 2022 pdf https openaccess thecvf com content wacv2022 papers guan m3detr multi representation multi scale mutual relation 3d object detection with transformers wacv 2022 paper pdf code https github com rayguan97 m3detr br group free 3d object detection via transformers iccv 2021 pdf https openaccess thecvf com content iccv2021 papers liu group free 3d object detection via transformers iccv 2021 paper pdf code https github com zeliu98 group free 3d br sa det3d self attention based context aware 3d object detection iccvw 2021 pdf https openaccess thecvf com content iccv2021w avvision papers bhattacharyya sa det3d self attention based context aware 3d object detection iccvw 2021 paper pdf code https github com autovision cloud sa det3d br 3d object detection with pointformer cvpr 2021 pdf http openaccess thecvf com content cvpr2021 papers pan 3d object detection with pointformer cvpr 2021 paper pdf code https github com vladimir2506 pointformer br temporal channel transformer for 3d lidar based video object detection in autonomous driving ieee trans circuits syst pdf https arxiv org pdf 2011 13628 br mlcvnet multi level context votenet for 3d object detection cvpr 2020 pdf https openaccess thecvf com content cvpr 2020 papers xie mlcvnet multi level context votenet for 3d object detection cvpr 2020 paper pdf code https github com nuaaxq mlcvnet br lidar based online 3d video object detection with graph based message passing and spatiotemporal transformer attention cvpr 2020 pdf http openaccess thecvf com content cvpr 2020 papers yin lidar based online 3d video object detection with graph based message passing cvpr 2020 paper pdf code https github com yinjunbo 3dvid br scanet spatial channel attention network for 3d object detection icassp 2019 pdf https ieeexplore ieee org document 8682746 code https github com zhouruqin scanet br 3d segmentation for part segmentation check object classification object classification complete scenes segmentation stratified transformer for 3d point cloud segmentation cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers lai stratified transformer for 3d point cloud segmentation cvpr 2022 paper pdf code https github com dvlab research stratified transformer br multimodal token fusion for vision transformers cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers wang multimodal token fusion for vision transformers cvpr 2022 paper pdf code https github com yikaiw tokenfusion br sparse cross scale attention network for efficient lidar panoptic segmentation aaai 2022 pdf https www aaai org aaai22papers aaai 5976 xus pdf br fast point transformer cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers park fast point transformer cvpr 2022 paper pdf code https github com postech cvlab fastpointtransformer br segment fusion hierarchical context fusion for robust 3d semantic segmentation cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers thyagharajan segment fusion hierarchical context fusion for robust 3d semantic segmentation cvpr 2022 paper pdf br point cloud video segmentation point spatio temporal transformer networks for point cloud video modeling tpami 2022 pdf https ieeexplore ieee org abstract document 9740525 br spatial temporal transformer for 3d point cloud sequences wacv 2022 pdf https openaccess thecvf com content wacv2022 papers wei spatial temporal transformer for 3d point cloud sequences wacv 2022 paper pdf br point 4d transformer networks for spatio temporal modeling in point cloud videos cvpr 2021 pdf http openaccess thecvf com content cvpr2021 papers fan point 4d transformer networks for spatio temporal modeling in point cloud cvpr 2021 paper pdf code https github com hehefan p4transformer br medical imaging segmentation swin unetr swin transformers for semantic segmentation of brain tumors in mri images miccai brainles 2022 pdf https arxiv org pdf 2201 01266 code https github com project monai research contributions tree master swinunetr brats21 br d former a u shaped dilated transformer for 3d medical image segmentation neural comput appl 2022 pdf https arxiv org pdf 2201 00462 br a robust volumetric transformer for accurate 3d tumor segmentation miccai 2022 pdf https arxiv org pdf 2111 13300 code https github com himashi92 vt unet br t automl automated machine learning for lesion segmentation using transformers in 3d medical imaging iccv 2021 pdf https openaccess thecvf com content iccv2021 papers yang t automl automated machine learning for lesion segmentation using transformers in iccv 2021 paper pdf br after unet axial fusion transformer unet for medical image segmentation wacv 2022 pdf https openaccess thecvf com content wacv2022 papers yan after unet axial fusion transformer unet for medical image segmentation wacv 2022 paper pdf br bitr unet a cnn transformer combined network for mri brain tumor segmentation miccai brainles 2022 pdf https arxiv org pdf 2109 12271 br nnformer interleaved transformer for volumetric segmentation arxiv 2021 pdf https arxiv org pdf 2109 03201 code https github com 282857341 nnformer br utnet a hybrid transformer architecture for medical image segmentation miccai 2022 pdf https arxiv org abs 2107 00781 code https github com yhygao utnet br medical image segmentation using squeezeand expansion transformers ijcai 2021 pdf https arxiv org pdf 2105 09511 code https github com askerlee segtran br unetr transformers for 3d medical image segmentation wacv 2022 pdf https openaccess thecvf com content wacv2022 papers hatamizadeh unetr transformers for 3d medical image segmentation wacv 2022 paper pdf code https github com project monai research contributions tree master unetr btcv br transbts multimodal brain tumor segmentation using transformer miccai 2021 pdf https arxiv org pdf 2103 04430 code https github com wenxuan 1119 transbts br spectr spectral transformer for hyperspectral pathology image segmentation arxiv 2021 pdf https arxiv org pdf 2103 03604 code https github com hfut xc yun spectr br cotr efficiently bridging cnn and transformer for 3d medical image segmentation miccai 2021 pdf https arxiv org pdf 2103 03024 code https github com ytongxie cotr br convolution free medical image segmentation using transformers miccai 2021 pdf https arxiv org pdf 2102 13645 br transfuse fusing transformers and cnns for medical image segmentation miccai 2021 pdf https arxiv org pdf 2102 08005 code https github com rayicer transfuse br 3d point cloud completion learning local displacements for point cloud completion cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers wang learning local displacements for point cloud completion cvpr 2022 paper pdf code https github com wangyida disp3d br autosdf shape priors for 3d completion reconstruction and generation cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers mittal autosdf shape priors for 3d completion reconstruction and generation cvpr 2022 paper pdf code https github com yccyenchicheng autosdf br pointattn you only need attention for point cloud completion arxiv 2022 pdf https arxiv org pdf 2203 08485 code https github com ohhhyeahhh pointattn br point cloud completion on structured feature map with feedback network cvm 2022 pdf https arxiv org pdf 2202 08583 br shapeformer transformer based shape completion via sparse representation cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers yan shapeformer transformer based shape completion via sparse representation cvpr 2022 paper pdf code https github com qheldiv shapeformer br a conditional point diffusion refinement paradigm for 3d point cloud completion iclr 2021 pdf https arxiv org pdf 2112 03530 code https github com zhaoyanglyu point diffusion refinement br mfm net unpaired shape completion network with multi stage feature matching arxiv 2021 pdf https arxiv org pdf 2111 11976 br pctma net point cloud transformer with morphing atlas based point generation network for dense point cloud completion iros 2021 pdf https www researchgate net profile alexander perzylo publication 353955048 pctma net point cloud transformer with morphing atlas based point generation network for dense point cloud completion links 611bd6930c2bfa282a50001d pctma net point cloud transformer with morphing atlas based point generation network for dense point cloud completion pdf code https github com linjianjie pctma net br pointr diverse point cloud completion with geometry aware transformers iccv 2021 pdf http openaccess thecvf com content iccv2021 papers yu pointr diverse point cloud completion with geometry aware transformers iccv 2021 paper pdf code https github com yuxumin pointr br snowflakenet point cloud completion by snowflake point deconvolution with skip transformer iccv 2021 pdf http openaccess thecvf com content iccv2021 papers xiang snowflakenet point cloud completion by snowflake point deconvolution with skip transformer iccv 2021 paper pdf code https github com allenxiangx snowflakenet br 3d pose estimation permutation invariant relational network for multi person 3d pose estimation arxiv 2022 pdf https arxiv org pdf 2204 04913 br zero shot category level object pose estimation eccv 2022 pdf https arxiv org pdf 2204 03635 code https github com applied ai lab zero shot pose br efficient virtual view selection for 3d hand pose estimation aaai 2022 pdf https www aaai org aaai22papers aaai 1352 chengj pdf code https github com iscas3dv handpose virtualview br learning based point cloud registration for 6d object pose estimation in the real world eccv 2022 pdf https infoscience epfl ch record 295132 files eccv2022 match normalisation point cloud registration new pdf code https github com dangzheng matchnorm br crossformer cross spatio temporal transformer for 3d human pose estimation arxiv 2022 pdf https arxiv org pdf 2203 13387 code https github com mfawzy crossformer br raytran 3d pose estimation and shape reconstruction of multiple objects from videos with ray traced transformers eccv 2022 pdf https arxiv org pdf 2203 13296 br p stmo pre trained spatial temporal many to one model for 3d human pose estimation eccv 2022 pdf https arxiv org pdf 2203 07628 code https github com patrick swk p stmo br mixste seq2seq mixed spatio temporal encoder for 3d human pose estimation in video cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers zhang mixste seq2seq mixed spatio temporal encoder for 3d human pose estimation cvpr 2022 paper pdf code https github com jinluzhang1126 mixste br 6d vit category level 6d object pose estimation via transformer based instance representation learning tip 2022 pdf https arxiv org pdf 2110 04792 br keypoint transformer solving joint identification in challenging hands and object interactions for accurate 3d pose estimation cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers hampali keypoint transformer solving joint identification in challenging hands and object cvpr 2022 paper pdf code https github com shreyashampali kypt transformer br exploiting temporal contexts with strided transformer for 3d human pose estimation ieee trans multimed 2022 pdf https arxiv org pdf 2103 14304 code https github com vegetebird stridedtransformer pose3d br 3d human pose estimation with spatial and temporal transformers iccv 2021 pdf http openaccess thecvf com content iccv2021 papers zheng 3d human pose estimation with spatial and temporal transformers iccv 2021 paper pdf code https github com zczcwh poseformer br end to end human pose and mesh reconstruction with transformers cvpr 2021 pdf http openaccess thecvf com content cvpr2021 papers lin end to end human pose and mesh reconstruction with transformers cvpr 2021 paper pdf code https github com microsoft meshtransformer br pi net pose interacting network for multi person monocular 3d pose estimation wacv 2021 pdf http openaccess thecvf com content wacv2021 papers guo pi net pose interacting network for multi person monocular 3d pose estimation wacv 2021 paper pdf code https github com guo w pi net br hot net non autoregressive transformer for 3d hand object pose estimation acm mm 2020 pdf https dl acm org doi pdf 10 1145 3394171 3413775 br hand transformer non autoregressive structured modeling for 3d hand pose estimation eccv 2020 pdf https cse buffalo edu jsyuan papers 2020 4836 pdf br epipolar transformer for multi view human pose estimation cvprw 2020 pdf http openaccess thecvf com content cvprw 2020 papers w70 he epipolar transformer for multi view human pose estimation cvprw 2020 paper pdf code https github com yihui he epipolar transformers br other tasks 3d tracking pttr relational 3d point cloud object tracking with transformer cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers zhou pttr relational 3d point cloud object tracking with transformer cvpr 2022 paper pdf code https github com jasonkks pttr br 3d object tracking with transformer bmvc 2021 pdf https arxiv org pdf 2110 14921 br 3d motion prediction hr stan high resolution spatio temporal attention network for 3d human motion prediction cvprw 2022 pdf https openaccess thecvf com content cvpr2022w precognition papers medjaouri hr stan high resolution spatio temporal attention network for 3d human motion prediction cvprw 2022 paper pdf br gimo gaze informed human motion prediction in context eccv 2022 pdf https arxiv org pdf 2204 09443 code https github com y zheng18 gimo br pose transformers potr human motion prediction with non autoregressive transformer iccvw 2021 pdf https openaccess thecvf com content iccv2021w somof papers martinez gonzalez pose transformers potr human motion prediction with non autoregressive transformers iccvw 2021 paper pdf code https github com idiap potr br learning progressive joint propagation for human motion prediction eccv 2020 pdf https www ecva net papers eccv 2020 papers eccv papers 123520222 pdf br history repeats itself human motion prediction via motion attention eccv 2020 pdf https arxiv org pdf 2007 11755 code https github com wei mao 2019 hisrepitself br a spatio temporal transformer for 3d human motion prediction 3dv 2021 pdf https arxiv org pdf 2004 08692 code https github com eth ait motion transformer br 3d reconstruction vpfusion joint 3d volume and pixel aligned feature fusion for single and multi view 3d reconstruction arxiv 2022 pdf https arxiv org pdf 2203 07553 br thundr transformer based 3d human reconstruction with marker iccv 2021 pdf http openaccess thecvf com content iccv2021 papers zanfir thundr transformer based 3d human reconstruction with markers iccv 2021 paper pdf br multi view 3d reconstruction with transformer iccv 2021 pdf https openaccess thecvf com content iccv2021 papers wang multi view 3d reconstruction with transformers iccv 2021 paper pdf br point cloud registration regtr end to end point cloud correspondences with transformer cvpr 2022 pdf https openaccess thecvf com content cvpr2022 papers yew regtr end to end point cloud correspondences with transformers cvpr 2022 paper pdf code https github com yewzijian regtr br legoformer transformers for block by block multi view 3d reconstruction cvpr 2021 pdf https arxiv org abs 2106 12102 code https github com faridyagubbayli legoformer br robust point cloud registra tion framework based on deep graph matching cvpr 2021 pdf https openaccess thecvf com content cvpr2021 papers fu robust point cloud registration framework based on deep graph matching cvpr 2021 paper pdf code https github com fukexue rgm br deep closest point learning representations for point cloud registration iccv 2019 pdf https openaccess thecvf com content iccv 2019 papers wang deep closest point learning representations for point cloud registration iccv 2019 paper pdf code https github com wangyueft dcp br citation if you find the listing or the survey useful for your work please cite our paper misc lahoud20223d title 3d vision with transformers a survey author lahoud jean and cao jiale and khan fahad shahbaz and cholakkal hisham and anwer rao muhammad and khan salman and yang ming hsuan year 2022 eprint 2208 04309 archiveprefix arxiv primaryclass cs cv | ai |
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beg-hybrid-mobile-app-dev | apress source code this repository accompanies beginning hybrid mobile application development http www apress com 9781484213155 by mahesh panhale apress 2016 cover image 9781484213155 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository | front_end |
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Cloud-Data-Engineering | cloud data engineering cloud data engineering specialization in coursera | cloud |
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FreeRTOS-GDB | freertos gdb python api library for inspecting freertos objects in gdb basically the freertos internal state is kind of hard to inspect when working with gdb this project provides some scripts for gdb s python api that make accessing some of these internals a little easier to inspect requirements 1 you need to have the python api enabled in your version of gdb this is a compile time option when building gdb you should be able to do something like this gdb python print hello world and get predictable results if it throws an error then you don t have python compiled in your version of gdb 2 need to be using freertos 8 0 this code could probably be used with freertos version 7 0 or previous versions but the current code doesn t support it 3 you need to use the handle registry for queue info to be any use note that this only works for queue based objects and not for eventgroups 4 you need to put the freertos gdb src directory on your python path export pythonpath src freertos gdb src how to use gdb bin program elf gdb c program runs on embedded device sets up tasks and queues break gdb source freertos gdb src freertos py gdb show task list name pri stck ready list 0 num tasks 1 idle 0 107 blocked list num tasks 11 emac task 1 239 afec0 task 1 295 ldsens task 1 195 afec1 task 1 295 linesample task 1 281 dmauart0 1 225 log task 1 273 bat task 1 169 rx task 1 421 mng task 2 551 cell task 1 275 delayed 1 num tasks 5 tmr svc 3 355 62254 wlan task 1 205 13817 init task 1 445 10015 led task 1 179 7105 dmacom1 1 265 7065 delayed 2 num tasks 0 gdb show queue info mutex num queues 6 name cnt send receive log lock 1 none none stream lock 1 none none twi mutex 1 none none cc3000 lock 1 none none wlan lock 0 none none spi lock 1 none none gdb show queue info queue num queues 14 name cnt send receive tmrq 0 tmr svc log msgpool 12 none none log msgq 0 log task twi queue 0 none none spi queue 0 none none dmaafec pool 1 none none dmaafec queue 0 afec0 task dmaafec pool 1 none none dmaafec queue 0 afec1 task com txpool 3 none none com txq 0 none none com rxpool 5 none none com rxq 0 none none fatfs mutex 0 none none note the none s above may just be empty strings this code adds the following custom gdb commands show list handle symbol address casttype casttype is an optional argument that will cast all of the handles in a list to a particular type show task list show handle registry show handle name symbole address show queue info filter filter can be queue mutex semaphore counting recursive todo with gdb s python api it doesn t seem to handle code is separate files very well currently the eventgroup objects don t have an inspector work in progress ideal solution would likely modify the struct of the event group to provide a similar piece of info that the queue handle does so that we could use the same registry | os |
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ACL2022_KnowledgeNLP_Tutorial | acl2022 knowledgenlp tutorial https img shields io badge status building brightgreen https img shields io badge prs welcome red materials for acl2022 https www 2022 aclweb org tutorial knowledge augmented methods for natural language processing time and location 1 time 9 30am 1 00pm may 22th 2022 gmt 2 location wicklow hall 2 convention centre dublin 3 zoom click join zoom room at underline tutorial abstract pdf files knowledge tutorial pdf knowledge in nlp has been a rising trend especially after the advent of large scale pre trained models nlp models with attention to knowledge can i access unlimited amount of external information ii delegate the task of storing knowledge from its parameter space to knowledge sources iii obtain up to date information iv make prediction results more explainable via selected knowledge in this tutorial we will introduce the key steps in integrating knowledge into nlp including knowledge grounding from text knowledge representation and fusing we will also introduce recent state of the art applications in fusing knowledge into language understanding language generation and commonsense reasoning if you find this tutorial helpful for your work please kindly cite our paper inproceedings zhu2022knowledge title knowledge augmented methods for natural language processing author zhu chenguang and xu yichong and ren xiang and lin bill and jiang meng and yu wenhao booktitle proceedings of the 60th annual meeting of the association for computational linguistics tutorial abstracts pages 12 20 year 2022 tutorial materials 1 slides introduction files part1 introduction pdf knowledgefornlu files part2 knowledge nlu pdf knowledgefornlg files part3 knowledge nlg pdf knowledgeforcommonsense files part4 knowledge cr pdf conclusion files part5 conclusion pdf 2 video allparts https underline io events 284 sessions eventsessionid 10737 3 survey a survey of knowledge enhanced text generation in acm computing survey cusr 2022 pdf https arxiv org abs 2010 04389 a survey of knowledge enhanced pre trained models on arxiv 2021 pdf https arxiv org pdf 2110 00269 pdf pre train prompt and predict a systematic survey of prompting methods in natural language processing on arxiv 2021 pdf https arxiv org pdf 2107 13586 pdf a survey on retrieval augmented text generation on arxiv 2022 pdf https arxiv org pdf 2202 01110 pdf 4 reading list kagnet knowledge aware graph networks for commonsense reasoning in emnlp 2019 pdf https aclanthology org d19 1282 birds have four legs numersense probing numerical commonsense knowledge of pre trained language models in emnlp 2020 pdf https aclanthology org 2020 emnlp main 557 differentiable open ended commonsense reasoning in naacl 2021 pdf https aclanthology org 2021 naacl main 366 commongen a constrained text generation challenge for generative commonsense reasoning in emnlp 2021 pdf https aclanthology org 2020 findings emnlp 165 kg fid infusing knowledge graph in fusion in decoder for open domain question answering in acl 2022 pdf https arxiv org abs 2110 04330 dict bert enhancing language model pre training with dictionary in acl 2022 pdf https arxiv org abs 2110 06490 fusing context into knowledge graph for commonsense question answering in acl 2021 pdf https arxiv org abs 2012 04808 retrieval enhanced model for commonsense generation in acl 2021 pdf https arxiv org abs 2105 11174 diversifying content generation for commonsense reasoning with mixture of knowledge graph experts in acl 2022 pdf https www microsoft com en us research uploads prod 2022 03 acl 2022 dgr camera pdf jaket joint pre training of knowledge graph and language understanding in aaai 2022 pdf https arxiv org abs 2010 00796 boosting factual correctness of abstractive summarization with knowledge graph in naacl 2021 pdf https arxiv org abs 2003 08612 tutorial schedule local time gmt content presenter slides 09 30 09 45 motivation and introduction of knowledge in nlp chenguang zhu slides files part1 introduction pdf 09 45 10 35 knowledge in natural language understanding yichong xu slides files part2 knowledge nlu pdf 10 35 11 00 knowledge in natural language generation wenhao yu slides files part3 knowledge nlg pdf 11 00 11 30 coffee break 11 30 11 55 knowledge in natural language generation wenhao yu cont 11 55 12 45 commonsense knowledge and reasoning for nlp yuchen lin xiang ren slides files part4 knowledge cr pdf 12 45 13 00 summary and future direction meng jiang slides files part5 conclusion pdf presenters nbsp img src imgs chenguang jpg width 120 align center nbsp img src imgs yichong jpg width 120 align center nbsp img src imgs xiang jpg width 120 align center nbsp img src imgs yuchen jpg width 120 align center nbsp img src imgs meng jpg width 120 align center nbsp img src imgs wenhao jpg width 120 align center chenguang zhu nbsp nbsp nbsp nbsp nbsp yichong xu nbsp nbsp nbsp nbsp nbsp nbsp nbsp xiang ren nbsp nbsp nbsp nbsp nbsp nbsp yuchen lin nbsp nbsp nbsp nbsp nbsp nbsp meng jiang nbsp nbsp nbsp nbsp nbsp nbsp wenhao yu chenguang zhu https www microsoft com en us research people chezhu is a principal research manager in microsoft cognitive services research group where he leads the knowledge language team his research in nlp covers knowledge graph text summarization and task oriented dialogue he has led teams to achieve first places in multiple nlp competitions including commonsenseqa commongen fever coqa arc and squad v1 0 he holds a ph d degree in computer science from stanford university dr zhu has given talks at stanford university carnegie mellon university and university of notre dame he has previously been ta for coursera online class automata giving teaching sessions to 100k international students yichong xu https www microsoft com en us research people yicxu is a senior researcher in knowledge language team in microsoft cognitive services research group his research in nlp focuses on using external knowledge to help natural language processing including question answering commonsense reasoning and text summarization dr xu received his ph d in machine learning from carnegie mellon university during his time at cmu he has been ta for large classes 200 students on machine learning and convex optimization dr xu has given talks at cmu ai seminar as well as in many international conferences including acl naacl neurips icml etc xiang ren https shanzhenren github io is an assistant professor at the usc computer science department a research team leader at usc isi and the pi of the intelligence and knowledge discovery ink lab at usc priorly he received his ph d in computer science from the university of illinois urbana champaign dr ren works on knowledge acquisition and reasoning in natural language processing with focuses on developing human centered and label efficient computational methods for building trustworthy nlp systems ren publishes over 100 research papers and delivered over 10 tutorials at the top conferences in natural language process data mining and artificial intelligence he received nsf career award the web conference best paper runner up acm sigkdd doctoral dissertation award and several research awards from google amazon jp morgan sony and snapchat he was named forbes asia 30 under 30 in 2019 bill yuchen lin https yuchenlin xyz is a ph d candidate at usc his research goal is to teach machines to think talk and act with commonsense knowledge and commonsense reasoning ability as humans do towards this ultimate goal he has been developing knowledge augmented reasoning methods e g kagnet mhgrn drfact and constructing benchmark datasets e g commongen riddlesense x csr that require commonsense knowledge and complex reasoning for both nlu and nlg he initiated an online compendium of commonsense reasoning research which serves as a portal for the community meng jiang http www meng jiang com is currently an assistant professor at the department of computer science and engineering in the university of notre dame he obtained his b e and ph d from tsinghua university he spent two years in uiuc as a postdoc and joined nd in 2017 his research interests include data mining machine learning and natural language processing he has published more than 100 peer reviewed papers of these topics he is the recipient of notre dame international faculty research award the honors and awards he received include best paper finalist in kdd 2014 best paper award in kdd dlg workshop 2020 and acm sigsoft distinguished paper award in icse 2021 he received nsf crii award in 2019 and career award in 2022 wenhao yu https wyu97 github io is a ph d candidate in the department of computer science and engineering at the university of notre dame his research lies in controllable knowledge driven natural language processing and generation his research has been published in top ranked nlp and data mining conferences such as acl emnlp and kdd | ai |
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cmake-kconfig | cmake kconfig minimal cmake project with kconfig integration adapted from zephyr example default build using a provided configurations called test mkdir build cd build cmake gninja dboard test ninja note the above uses the config provided by configs test defconfig updating the configuration ninja menuconfig this will bring up an interactive menu to turn options on off and it will save a config file in the build directory the test defconfig can be updated by copying the build config file to configs test defconfig and committing before any targets are built an autoconf h header file is generated under build kconfig include generate autoconf h this is allows everything to have a common configuration cmake if config test option message config test option is enabled endif make include build config ifeq config test option y objs src test option o endif c c include autoconf h ifdef config test option code built for option endif kconfig kconfig is brilliant it manages a unified configuration separately from the main source code that can be used with the build system and source code it is the best in class configuration management tool that exists for embedded c code period it allows dependencies to be defined between different config options and the best thing is some really smart people have worked all this out before so we get a really powerful system for little effort cost | cmake kconfig embedded c configuration configuration-management | os |
Digital-System-Design-and-Generation | digital system design and generation system verilog projects this repository contains 3 digital design projects each folder contains the problem statement and the reports for the projects the projects are system verilog codes for asic design of modules as described below a brief description of the projects is given below 1 multiply and accumulate this is a basic multiply and accumulation unit that implements the equation f f a b the project is just for exploring system verilog and getting familiar with concepts of asic design the effects of pipeling with different stages and at different places are studied and analysis of area critical path clock and power is done a testbench is also implemented which consists of a c file that generates test case inputs that are then fed into the design developed the c file also generates the exepcted output values for the sample input given the results from the design are then compared with the expected output values to verify operation of the design 2 matrix vector multiplication a matrix vector multiplication for multiplying 3x3 matrix with a vector and similarly for 4x4 matrix witha a vector are implemented a basic implementation is done followed by various optimized implementations for optimizing delay power and area are done the optimizations are done by either pipelining parallelization or modifying control logic to produce a better design a testbench is also written to verify operation and functionality 3 hardware generation tool this project is a code generation tool that generates system verilog code for implementation of a matrix vector multiplier it takes as inputs from the user the following specifications for the matrix vector multiplier number of bits precision matrix and vector dimensions degree of parallelism and pipelining options based on the inputs given by the user the tool generates the system verliog code for the implementation of the system with the input specifications also a testbench is also generated automatically to verify the design generated and produced performance reports of critical path timing analysis area and power are auto generated and stored the report presents the implications of the design choices as well as the analysis of the tool in terms of efficiency of design and the performance parameters | os |
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gcp-iot-core-examples | microchip examples for google cloud platform iot core summary this project contains examples and tools for setting up a secure iot node with iot core using the atecc508a or atecc608a and winc1500 with a selection of energy efficent microcontrollers security devices atecc508a http www microchip com wwwproducts en atecc508a atecc608a http www microchip com wwwproducts en atecc608a connectivity atwinc1500 http www microchip com wwwproducts en atwinc1500 802 11 b g n module with integrated tls 1 2 stack atsamw25 http www microchip com wwwproducts en atsamw25 integrated module featuring atwinc1500 atsamd21g18a atecc508a microcontrollers or socs atsamd21 arm cortex m0 atsamg55 arm cortex m4 atsamw25 arm cortex m0 raspberry pi project organization these examples were built with atmel studio 7 http www atmel com microsite atmel studio atsamd21 quick start boards gcp iot core samd21 atsln atsamg55 quick start boards gcp iot core samg55 atsln atsamw25 quick start boards gcp iot core samw25 atsln getting started the first step is ensuring that you have a google cloud iot core https console cloud google com account set up with iot core the tutorials will walk you through the initial configuration of your account and creation of your first device registry 1 iot core getting started guide https cloud google com iot docs how tos getting started 2 iot core quick start https cloud google com iot docs quickstart examples fan controller this example features a number of interconnected devices to demonstrate data acquisition control and integration into google iot core hardware featured atecc508a security cryptographic authentication atwinc1500 wifi connectivity emc1414 temperature sensor emc2301 5v pwm fan controller license for more details of the included software and licenses please reference license md the included software is covered by the microchip license with the exception of the following asf headers and winc1500 driver software are covered by the bsd 3 clause license eclipse paho mqtt client is covered by the eclipse distribution license v 1 0 bsd 3 clause parson json c library is covered by the mit free software license | iot microchip google-iot gcp-iot atecc508 atecc608 mqtt tls winc1500 security cryptography elliptic-curves | server |
iota.c | h1 align center br img src iota c png a h1 h2 align center the official c client library for interacting with the tangle h2 p align center a href https iota c client readthedocs io en latest index html style text decoration none img src https img shields io badge documentation 20portal blue svg style for the badge alt developer documentation p p align center a href https discord iota org style text decoration none img src https img shields io badge discord 9cf svg logo discord alt discord a a href https iota stackexchange com style text decoration none img src https img shields io badge stackexchange 9cf svg logo stackexchange alt stackexchange a a href https github com iotaledger iota c blob master license style text decoration none img src https img shields io github license iotaledger iota c svg alt apache 2 0 license a a href https iota c client readthedocs io en latest index html style text decoration none img src https img shields io readthedocs iota c client alt client api reference a p p align center a href about about a a href prerequisites prerequisites a a href building iota c client library building client library a a href building documentation building document a a href visual studio code integration visual studio code integration a a href supporting the project supporting the project a a href joining the discussion joining the discussion a p about this is the official c client library which allows you to do the following create blocks read blocks sign transactions generate addresses implement a wallet application you can find api reference on c client api reference https iota c client readthedocs io en latest index html this is in highly development so there may be performance and stability issues please report any issues in issues https github com iotaledger iota c issues or discussions https github com iotaledger iota c discussions prerequisites pkg config libcurl4 openssl dev gcc or clang cmake https cmake org 3 15 and above ninja build https ninja build org optional ubuntu shell sudo apt install build essential libcurl4 openssl dev pkg config building iota c client library iota c client support different crypto libraries including openssl libsodium and mbedtsl it can be changed by cmake property cryptouse fro example adding dcryptouse libsodium to use libsodium compiling and testing library with clang11 and ninja shell git clone https github com iotaledger iota c git cd iota c mkdir build cd build cmake g ninja dcmake c compiler clang 11 dcmake cxx compiler clang 11 diota wallet enable bool true dcmake install prefix pwd ninja ninja test compiling and testing library with make and gcc shell git clone https github com iotaledger iota c git cd iota c mkdir build cd build cmake dcmake c compiler gcc dcmake cxx compiler g diota wallet enable bool true dcmake install prefix pwd make j8 make test compiling and testing library with default compiler shell git clone https github com iotaledger iota c git cd iota c mkdir build cd build cmake diota wallet enable bool true dcmake install prefix pwd make make test the default build type is debug mode with debug symbols for release mode you can add dcmake build type release during the cmake configuration stage building documentation the documentation is automatically generated thought doxygen and sphinx tools this steps are tested on ubuntu please refer to installation guides for different platforms install sphinx https www sphinx doc org en master usage installation html install doxygen https www doxygen nl manual install html shell sudo apt install doxygen python3 sphinx python3 pip git clone https github com iotaledger iota c git cd iota c docs pip3 install r requirements txt doxygen doxyfile make html the documentation will locate at docs build html index html in the project root directory visual studio code integration this template includes launch configurations for debugging test cases with visual studio code located in the vscode directory see vscode readme md vscode readme md for more information if you re not using vs code you can safely delete or ignore the directory supporting the project if the iota c client library has been useful to you and you feel like contributing consider posting a bug report https github com iotaledger iota c issues new issue feature request or a pull request https github com iotaledger iota c pulls we have some basic contribution guidelines github contributing md to keep our code base stable and consistent joining the discussion if you want to get involved in the community need help with getting set up have any issues related with the library or just want to discuss blockchain distributed ledgers and iot with other people feel free to join our discord https discord iota org | server |
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nlp | natural language processing experiments this is a repo of some random nlp experiments for fun the coolest is probably an automated text summarization engine you can see a live demo here http nlpsummarize herokuapp com here s a brief presentation http www slideshare net rzendacott automated text summarization on how the algorithm works bitdeli badge https d2weczhvl823v0 cloudfront net ryan endacott nlp trend png https bitdeli com free bitdeli badge | ai |
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mobile | mobile mobile development | front_end |
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pinephone-emulator | emulate pinephone with unicorn emulator read the articles possibly emulate pinephone with unicorn emulator https lupyuen github io articles unicorn clickable call graph for apache nuttx real time operating system https lupyuen github io articles unicorn2 we re porting a new operating system apache nuttx rtos https lupyuen github io articles what to pine64 pinephone https wiki pine64 org index php pinephone and i wondered to make pinephone testing easier can we emulate arm64 pinephone with unicorn emulator https www unicorn engine org let s find out we ll call the unicorn emulator https www unicorn engine org in rust instead of c because i m too old to write meticulous c but i m ok to get nagged by rust compiler if i miss something we begin by emulating simple arm64 machine code emulate arm64 machine code emulate arm64 machine code https lupyuen github io images unicorn code png suppose we wish to emulate some arm64 machine code https github com lupyuen pinephone emulator blob bc5643dea66c70f57a150955a12884f695acf1a4 src main rs l7 l8 here s our rust program that calls unicorn emulator to emulate the arm64 machine code https github com lupyuen pinephone emulator blob bc5643dea66c70f57a150955a12884f695acf1a4 src main rs l1 l55 we add unicorn engine to cargo toml cargo toml https github com lupyuen pinephone emulator blob bc5643dea66c70f57a150955a12884f695acf1a4 cargo toml l8 l9 and we run our rust program text cargo run verbose fresh cc v1 0 79 fresh cmake v0 1 49 fresh pkg config v0 3 26 fresh bitflags v1 3 2 fresh libc v0 2 139 fresh unicorn engine v2 0 1 fresh pinephone emulator v0 1 0 finished dev unoptimized debuginfo target s in 0 08s running target debug pinephone emulator our rust program works ok for emulating arm64 memory and arm64 registers let s talk about arm64 memory mapped input output memory access hook for arm64 emulation memory access hook for arm64 emulation https lupyuen github io images unicorn code2 png how will we emulate arm64 memory mapped input output unicorn emulator lets us attach hooks to emulate memory access here s a hook function for memory access https github com lupyuen pinephone emulator blob 3655ac2875664376f42ad3a3ced5cbf067790782 src main rs l83 l95 our hook function prints all read write access to emulated arm64 memory return value is unused https github com unicorn engine unicorn blob dev docs faq md i cant recover from unmapped readwrite even i return true in the hook why this is how we attach the hook function to the unicorn emulator https github com lupyuen pinephone emulator blob 3655ac2875664376f42ad3a3ced5cbf067790782 src main rs l59 l74 when we run our rust program we see the read and write memory accesses made by our emulated arm64 code https github com lupyuen pinephone emulator blob bc5643dea66c70f57a150955a12884f695acf1a4 src main rs l7 l8 text hook memory mem type write address 0x10008 size 4 value 0x12345678 hook memory mem type read address 0x10008 size 1 value 0x0 this memory access hook function will be helpful when we emulate memory mapped input output on pinephone like for the allwinner a64 uart controller unicorn emulator allows code execution hooks too code execution hook for arm64 emulation code execution hook for arm64 emulation https lupyuen github io images unicorn code3 png can we intercept every arm64 instruction that will be emulated yep we can call unicorn emulator to add a code execution hook here s a sample hook function that will be called for every arm64 instruction https github com lupyuen pinephone emulator blob 3655ac2875664376f42ad3a3ced5cbf067790782 src main rs l108 l117 and this is how we call unicorn emulator to add the above hook function https github com lupyuen pinephone emulator blob 3655ac2875664376f42ad3a3ced5cbf067790782 src main rs l52 l57 when we run our rust program we see the address of every arm64 instruction emulated and its size text hook code address 0x10000 size 4 hook code address 0x10004 size 4 we might use this to emulate special arm64 instructions if we don t need to intercept every single instruction try the block execution hook block execution hook for arm64 emulation is there something that works like a code execution hook but doesn t stop at every single arm64 instruction yep unicorn emulator supports block execution hooks this hook function will be called once when executing a block of arm64 instructions https github com lupyuen pinephone emulator blob 3655ac2875664376f42ad3a3ced5cbf067790782 src main rs l97 l106 this is how we add the block execution hook https github com lupyuen pinephone emulator blob 3655ac2875664376f42ad3a3ced5cbf067790782 src main rs l48 l50 when we run the rust program we see that that the block size is 8 text hook block address 0x10000 size 8 which means that unicorn emulator calls our hook function only once for the entire block of 2 arm64 instructions this block execution hook will be super helpful for monitoring the execution flow of our emulated code let s talk about the block what is a block of arm64 instructions what exactly is a block of arm64 instructions when we run this code from apache nuttx rtos that handles uart output text section func text up lowputc ldr x15 uart0 base address 400801f0 580000cf ldr x15 40080208 up lowputc 0x18 nuttx arch arm64 src chip a64 lowputc s 89 early uart ready x15 w2 400801f4 794029e2 ldrh w2 x15 20 400801f8 721b005f tst w2 0x20 400801fc 54ffffc0 b eq 400801f4 up lowputc 0x4 b none nuttx arch arm64 src chip a64 lowputc s 90 early uart transmit x15 w0 40080200 390001e0 strb w0 x15 nuttx arch arm64 src chip a64 lowputc s 91 ret 40080204 d65f03c0 ret arm64 disassembly https github com lupyuen pinephone emulator blob a1fb82d829856d86d6845c477709c2be24373aca nuttx nuttx s l3398 l3411 source code https github com apache nuttx blob master arch arm64 src a64 a64 lowputc s l61 l71 we observe that unicorm emulator treats 400801f0 to 400801fc as a block of arm64 instructins text hook block address 0x400801f0 size 16 hook code address 0x400801f0 size 4 hook code address 0x400801f4 size 4 hook code address 0x400801f4 size 4 hook code address 0x400801f8 size 4 hook code address 0x400801fc size 4 hook block address 0x400801f4 size 12 hook code address 0x400801f4 size 4 hook code address 0x400801f8 size 4 hook code address 0x400801fc size 4 hook block address 0x400801f4 size 12 hook code address 0x400801f4 size 4 hook code address 0x400801f8 size 4 hook code address 0x400801fc size 4 source https github com lupyuen pinephone emulator blob cd030954c2ace4cf0207872f275abc3ffb7343c6 readme md block execution hooks for arm64 emulation the block ends at 400801fc because there s an arm64 branch instruction b eq from this we deduce that unicorn emulator treats a sequence of arm64 instructions as a block until it sees a branch instruction including function calls unmapped memory in unicorn emulator what happens when unicorn emulator tries to access memory that isn t mapped unicorn emulator will call our memory access hook with mem type set to read unmapped text hook memory address 0x01c28014 size 2 mem type read unmapped value 0x0 source https github com lupyuen pinephone emulator blob b842358ba457b67ffa9f4c1a362b0386cfd97c4a readme md block execution hooks for arm64 emulation the log above says that address 0x01c2 8014 is unmapped this is how we map the memory https github com lupyuen pinephone emulator blob cd030954c2ace4cf0207872f275abc3ffb7343c6 src main rs l26 l32 see the nuttx memory map https github com apache nuttx blob master arch arm64 include a64 chip h l44 l52 can we map memory regions during emulation yep we may use a memory access hook to map memory regions on the fly see this https github com unicorn engine unicorn blob dev docs faq md i cant recover from unmapped readwrite even i return true in the hook why run apache nuttx rtos in unicorn emulator run apache nuttx rtos in unicorn emulator https lupyuen github io images unicorn code4 png let s run apache nuttx rtos in unicorn emulator we have compiled apache nuttx rtos for pinephone nuttx into an arm64 binary image nuttx bin this is how we load the nuttx binary image into unicorn https github com lupyuen pinephone emulator blob aa24d1c61256f38f92cf627d52c3e9a0c189bfc6 src main rs l6 l40 in our rust program above we mapped 2 memory regions for nuttx map 128 mb executable memory at 0x4000 0000 for arm64 machine code map 512 mb read write memory at 0x0000 0000 for memory mapped i o by allwinner a64 peripherals this is based on the nuttx memory map https github com apache nuttx blob master arch arm64 include a64 chip h l44 l52 for pinephone when we run this unicorn emulator loops forever let s find out why unicorn emulator waits forever for uart controller ready emulating the allwinner a64 uart controller https lupyuen github io images unicorn code5 png here s the output when we run nuttx rtos in unicorn emulator text hook memory address 0x01c28014 size 2 mem type read value 0x0 hook code address 0x400801f8 size 4 hook code address 0x400801fc size 4 hook block address 0x400801f4 size 12 hook code address 0x400801f4 size 4 hook memory address 0x01c28014 size 2 mem type read value 0x0 hook code address 0x400801f8 size 4 hook code address 0x400801fc size 4 hook block address 0x400801f4 size 12 hook code address 0x400801f4 size 4 hook memory address 0x01c28014 size 2 mem type read value 0x0 hook code address 0x400801f8 size 4 hook code address 0x400801fc size 4 hook block address 0x400801f4 size 12 hook code address 0x400801f4 size 4 source https github com lupyuen pinephone emulator blob 045fa5da84d9e07ead5a820a075c1445661328b6 readme md unicorn emulator waits forever for uart controller ready the above log shows that unicorn emulator loops forever at address 0x4008 01f4 while reading the data from address 0x01c2 8014 let s check the nuttx arm64 code at address 0x4008 01f4 text section func text up lowputc ldr x15 uart0 base address 400801f0 580000cf ldr x15 40080208 up lowputc 0x18 nuttx arch arm64 src chip a64 lowputc s 89 early uart ready x15 w2 400801f4 794029e2 ldrh w2 x15 20 400801f8 721b005f tst w2 0x20 400801fc 54ffffc0 b eq 400801f4 up lowputc 0x4 b none nuttx arch arm64 src chip a64 lowputc s 90 early uart transmit x15 w0 40080200 390001e0 strb w0 x15 nuttx arch arm64 src chip a64 lowputc s 91 ret 40080204 d65f03c0 ret arm64 disassembly https github com lupyuen pinephone emulator blob a1fb82d829856d86d6845c477709c2be24373aca nuttx nuttx s l3398 l3411 which comes from this nuttx source code text wait for a64 uart to be ready to transmit xb register that contains the uart base address wt scratch register number macro early uart ready xb wt 1 ldrh wt xb 0x14 uart lsr line status register tst wt 0x20 check thre tx holding register empty b eq 1b wait for the uart to be ready thre 1 endm source code https github com apache nuttx blob master arch arm64 src a64 a64 lowputc s l61 l71 this code waits for the uart controller to be ready before printing uart output by checking the value at 0x01c2 8014 the code is explained here wait for uart ready https lupyuen github io articles uboot wait for uart ready what is 0x01c2 8014 according to the allwinner a64 doc wait to transmit https lupyuen github io articles serial wait to transmit 0x01c2 8014 is the uart line status register uart lsr at offset 0x14 bit 5 needs to be set to 1 to indicate that the uart transmit fifo is ready we emulate the uart ready bit like so https github com lupyuen pinephone emulator blob 4d78876ad6f40126bf68cb2da4a43f56d9ef6e76 src main rs l42 l49 and unicorn emulator stops looping it continues execution to memset to init the bss section to 0 text hook block address 0x40089328 size 8 hook memory address 0x400b6a52 size 1 mem type write value 0x0 hook block address 0x40089328 size 8 hook memory address 0x400b6a53 size 1 mem type write value 0x0 hook block address 0x40089328 size 8 hook memory address 0x400b6a54 size 1 mem type write value 0x0 source https github com lupyuen pinephone emulator blob 045fa5da84d9e07ead5a820a075c1445661328b6 readme md unicorn emulator waits forever for uart controller ready but we don t see any uart output let s print the uart output emulate uart output in unicorn emulator emulating uart output in unicorn emulator https lupyuen github io images unicorn code6 png how do we print the uart output according to the allwinner a64 doc transmit uart https lupyuen github io articles serial transmit uart nuttx rtos will write the uart output to the uart transmit holding register thr at 0x01c2 8000 in our memory access hook let s intercept all writes to 0x01c2 8000 and dump the characters written to uart output https github com lupyuen pinephone emulator blob aa6dd986857231a935617e8346978d7750aa51e7 src main rs l89 l111 when we run this we see a long chain of uart output text cargo run grep uart uart output uart output uart output r uart output e uart output a uart output d uart output y source https gist github com lupyuen 587dbeb9329d9755e4d007dd8e1246cd which reads as text ready to boot cpu boot from el2 boot from el1 boot to c runtime for os initialize similar to this https lupyuen github io articles uboot pinephone boots nuttx yep nuttx rtos is booting on unicorn emulator but unicorn emulator halts while booting nuttx unicorn emulator halts in nuttx mmu unicorn emulator halts with an arm64 exception at address 4008 0ef8 text hook block address 0x40080cec size 16 hook code address 0x40080cec size 4 hook memory address 0x400c3f90 size 8 mem type read value 0x0 hook memory address 0x400c3f98 size 8 mem type read value 0x0 hook code address 0x40080cf0 size 4 hook memory address 0x400c3fa0 size 8 mem type read value 0x0 hook code address 0x40080cf4 size 4 hook memory address 0x400c3f80 size 8 mem type read value 0x0 hook memory address 0x400c3f88 size 8 mem type read value 0x0 hook code address 0x40080cf8 size 4 hook block address 0x40080eb0 size 12 hook code address 0x40080eb0 size 4 hook code address 0x40080eb4 size 4 hook code address 0x40080eb8 size 4 hook block address 0x40080ebc size 16 hook code address 0x40080ebc size 4 hook code address 0x40080ec0 size 4 hook code address 0x40080ec4 size 4 hook code address 0x40080ec8 size 4 hook block address 0x40080ecc size 16 hook code address 0x40080ecc size 4 hook code address 0x40080ed0 size 4 hook code address 0x40080ed4 size 4 hook code address 0x40080ed8 size 4 hook block address 0x40080edc size 12 hook code address 0x40080edc size 4 hook code address 0x40080ee0 size 4 hook code address 0x40080ee4 size 4 hook block address 0x40080ee8 size 4 hook code address 0x40080ee8 size 4 hook block address 0x40080eec size 16 hook code address 0x40080eec size 4 hook code address 0x40080ef0 size 4 hook code address 0x40080ef4 size 4 hook code address 0x40080ef8 size 4 err err exception see the complete log https gist github com lupyuen 778f15875edf632ccb5a093a656084cb unicorn emulator halts at the nuttx mmu el1 code at 0x4008 0ef8 text nuttx arch arm64 src common arm64 mmu c 544 write sysreg value sctlr m bit sctlr c bit sctlr el1 40080ef0 d28000a1 mov x1 0x5 5 40080ef4 aa010000 orr x0 x0 x1 40080ef8 d5181000 msr sctlr el1 x0 why did msr fail with an exception here s the context text enable mmu el1 nuttx arch arm64 src common arm64 mmu c 533 write sysreg memory attributes mair el1 40080ebc d2808000 mov x0 0x400 1024 40080ec0 f2a88180 movk x0 0x440c lsl 16 40080ec4 f2c01fe0 movk x0 0xff lsl 32 40080ec8 d518a200 msr mair el1 x0 nuttx arch arm64 src common arm64 mmu c 534 write sysreg get tcr 1 tcr el1 40080ecc d286a380 mov x0 0x351c 13596 40080ed0 f2a01000 movk x0 0x80 lsl 16 40080ed4 f2c00020 movk x0 0x1 lsl 32 40080ed8 d5182040 msr tcr el1 x0 nuttx arch arm64 src common arm64 mmu c 535 write sysreg uint64 t base xlat table ttbr0 el1 40080edc d00001a0 adrp x0 400b6000 g uart1port 40080ee0 91200000 add x0 x0 0x800 40080ee4 d5182000 msr ttbr0 el1 x0 arm64 isb nuttx arch arm64 src common barriers h 58 asm volatile isb memory 40080ee8 d5033fdf isb enable mmu el1 nuttx arch arm64 src common arm64 mmu c 543 value read sysreg sctlr el1 40080eec d5381000 mrs x0 sctlr el1 nuttx arch arm64 src common arm64 mmu c 544 write sysreg value sctlr m bit sctlr c bit sctlr el1 40080ef0 d28000a1 mov x1 0x5 5 40080ef4 aa010000 orr x0 x0 x1 40080ef8 d5181000 msr sctlr el1 x0 arm64 isb nuttx arch arm64 src common barriers h 58 40080efc d5033fdf isb which comes from this nuttx source code arm64 mmu c https github com apache nuttx blob master arch arm64 src common arm64 mmu c l541 l544 c enable the mmu and data cache read from system control register el1 value read sysreg sctlr el1 write to system control register el1 write sysreg write to system register value sctlr m bit sctlr c bit enable address translation and caching sctlr el1 system control register el1 let s dump the arm64 exception dump the arm64 exception to find out the cause of the arm64 exception let s dump the exception syndrome register esr but this won t work https github com lupyuen pinephone emulator blob 1cbfa48de10ef4735ebaf91ab85631cb48e37591 src main rs l86 l91 because esr el is no longer supported https github com unicorn engine unicorn blob master bindings rust src arm64 rs l288 l307 and cp reg can t be read in rust text err err exception cp reg err arg esr el0 ok 0 esr el1 ok 0 esr el2 ok 0 esr el3 ok 0 see the complete log https gist github com lupyuen 778f15875edf632ccb5a093a656084cb cp reg can t be read in rust because unicorn needs a pointer to uc arm64 cp reg c static uc err reg read cpuarmstate env unsigned int regid void value case uc arm64 reg cp reg ret read cp reg env uc arm64 cp reg value break source https github com unicorn engine unicorn blob master qemu target arm unicorn aarch64 c l225 l227 which isn t supported by the rust bindings https github com unicorn engine unicorn blob master bindings rust src lib rs l528 l543 works in python though https github com unicorn engine unicorn blob master bindings python sample arm64 py l76 l82 so instead we set a breakpoint at arm64 reg read pic below in text cargo registry src github com 1ecc6299db9ec823 unicorn engine 2 0 1 qemu target arm unicorn aarch64 c arm64 reg read calls reg read in unicorn aarch64 c which shows the exception as text env exception syndrome 0x8600 003f fsr 5 vaddress 0x400c 3fff target el 1 let s study the arm64 exception debug the arm64 exception https lupyuen github io images unicorn debug png arm64 mmu exception earlier we saw this arm64 exception in unicorn emulator text env exception syndrome 0x8600 003f fsr 5 vaddress 0x400c 3fff target el 1 todo what is address 0x400c 3fff what is syndrome 0x8600 003f bits 26 31 of syndrome 0b100001 which means 0b100001 instruction abort taken without a change in exception level used for mmu faults generated by instruction accesses and synchronous external aborts including synchronous parity or ecc errors not used for debug related exceptions source https developer arm com documentation ddi0601 2022 03 aarch64 registers esr el1 exception syndrome register el1 what is fsr 5 fsr 5 means 0b00101 translation fault in section source https developer arm com documentation ddi0500 d system control aarch64 register descriptions instruction fault status register el2 why the mmu fault unicorn emulator triggers the exception when nuttx writes to sctlr el1 c enable the mmu and data cache value read sysreg sctlr el1 write sysreg value sctlr m bit sctlr c bit sctlr el1 source https github com apache nuttx blob master arch arm64 src common arm64 mmu c l541 l544 the above code sets these flags in sctlr el1 system control register el1 sctlr m bit bit 0 enable address translation for el0 and el1 stage 1 sctlr c bit bit 2 enable caching for el0 and el1 stage 1 more about sctlr el1 https developer arm com documentation ddi0595 2021 06 aarch64 registers sctlr el1 system control register el1 todo why did the address translation or caching fail todo should we skip the mmu update to sctlr el1 since we don t use mmu debug the unicorn emulator to troubleshoot the arm64 mmu exception can we use a debugger to step through unicorn emulator yes but it gets messy to trace the exception in the debugger look for text home cargo registry src github com 1ecc6299db9ec823 unicorn engine 2 0 1 qemu target arm translate a64 c set a breakpoint in aarch64 tr translate insn which calls disas b exc sys which calls disas system which calls handle sys to handle system instructions to inspect the emulator settings set a breakpoint at cpu aarch64 init in text home cargo registry src github com 1ecc6299db9ec823 unicorn engine 2 0 1 qemu target arm cpu64 c todo check that the cpu setting is correct for pinephone cpu model should be cortex a53 map address to function with elf file read the article clickable call graph for apache nuttx real time operating system https lupyuen github io articles unicorn2 our block execution hook now prints the function name and the filename text hook block address 0x40080eb0 size 12 setup page tables arch arm64 src common arm64 mmu c 516 25 hook block address 0x40080eec size 16 enable mmu el1 arch arm64 src common arm64 mmu c 543 11 err err exception source https gist github com lupyuen f2e883b2b8054d75fbac7de661f0ee5a our hook function looks up the address in the dwarf debug symbols https crates io crates gimli of the nuttx elf file https github com lupyuen pinephone emulator blob main nuttx nuttx this is explained here map address to function with elf file https lupyuen github io articles unicorn appendix map address to function with elf file call graph for apache nuttx rtos to troubleshoot the apache nuttx mmu fault on unicorn emulator we auto generated this call graph to see the nuttx source code right click the node and select open link mermaid flowchart td start arm64 head arm64 head a64 lowputc click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l104 arch arm64 src common arm64 head s blank a64 lowputc arm64 head click a64 lowputc href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src a64 a64 lowputc s l58 arch arm64 src a64 a64 lowputc s blank arm64 head a64 lowputc click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l177 arch arm64 src common arm64 head s blank a64 lowputc arm64 head click a64 lowputc href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src a64 a64 lowputc s l87 arch arm64 src a64 a64 lowputc s blank arm64 head a64 lowputc click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l298 arch arm64 src common arm64 head s blank a64 lowputc arm64 head click a64 lowputc href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src a64 a64 lowputc s l87 arch arm64 src a64 a64 lowputc s blank arm64 head a64 lowputc click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l298 arch arm64 src common arm64 head s blank a64 lowputc arm64 head click a64 lowputc href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src a64 a64 lowputc s l87 arch arm64 src a64 a64 lowputc s blank arm64 head a64 lowputc click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l298 arch arm64 src common arm64 head s blank a64 lowputc arm64 head click a64 lowputc href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src a64 a64 lowputc s l87 arch arm64 src a64 a64 lowputc s blank arm64 head a64 lowputc click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l298 arch arm64 src common arm64 head s blank a64 lowputc arm64 head click a64 lowputc href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src a64 a64 lowputc s l87 arch arm64 src a64 a64 lowputc s blank arm64 head a64 lowputc click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l298 arch arm64 src common arm64 head s blank a64 lowputc arm64 head click a64 lowputc href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src a64 a64 lowputc s l87 arch arm64 src a64 a64 lowputc s blank arm64 head a64 lowputc click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l298 arch arm64 src common arm64 head s blank arm64 head arm64 boot el1 init click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l298 arch arm64 src common arm64 head s blank arm64 boot el1 init arm64 isb click arm64 boot el1 init href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 boot c l137 arch arm64 src common arm64 boot c blank arm64 isb arm64 boot el1 init click arm64 isb href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common barriers h l57 arch arm64 src common barriers h blank arm64 boot el1 init arm64 isb click arm64 boot el1 init href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 boot c l145 arch arm64 src common arm64 boot c blank arm64 isb arm64 boot el1 init click arm64 isb href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common barriers h l57 arch arm64 src common barriers h blank arm64 head arm64 boot primary c routine click arm64 head href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 head s l298 arch arm64 src common arm64 head s blank arm64 boot primary c routine memset click arm64 boot primary c routine href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 boot c l180 arch arm64 src common arm64 boot c blank memset arm64 boot primary c routine click memset href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b libs libc string lib memset c l168 libs libc string lib memset c blank arm64 boot primary c routine arm64 chip boot click arm64 boot primary c routine href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 boot c l181 arch arm64 src common arm64 boot c blank arm64 chip boot arm64 mmu init click arm64 chip boot href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src a64 a64 boot c l81 arch arm64 src a64 a64 boot c blank arm64 mmu init setup page tables click arm64 mmu init href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l583 arch arm64 src common arm64 mmu c blank setup page tables init xlat tables click setup page tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l490 arch arm64 src common arm64 mmu c blank init xlat tables calculate pte index click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l417 arch arm64 src common arm64 mmu c blank calculate pte index init xlat tables click calculate pte index href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l246 arch arm64 src common arm64 mmu c blank init xlat tables pte desc type click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank pte desc type new prealloc table click pte desc type href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l234 arch arm64 src common arm64 mmu c blank new prealloc table init xlat tables click new prealloc table href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l378 arch arm64 src common arm64 mmu c blank init xlat tables calculate pte index click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l437 arch arm64 src common arm64 mmu c blank calculate pte index pte desc type click calculate pte index href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l246 arch arm64 src common arm64 mmu c blank pte desc type calculate pte index click pte desc type href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l234 arch arm64 src common arm64 mmu c blank calculate pte index init xlat tables click calculate pte index href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l247 arch arm64 src common arm64 mmu c blank init xlat tables set pte block desc click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank set pte block desc init xlat tables click set pte block desc href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l293 arch arm64 src common arm64 mmu c blank init xlat tables calculate pte index click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l456 arch arm64 src common arm64 mmu c blank calculate pte index init xlat tables click calculate pte index href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l246 arch arm64 src common arm64 mmu c blank init xlat tables pte desc type click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank pte desc type init xlat tables click pte desc type href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l234 arch arm64 src common arm64 mmu c blank init xlat tables calculate pte index click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l472 arch arm64 src common arm64 mmu c blank calculate pte index pte desc type click calculate pte index href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l246 arch arm64 src common arm64 mmu c blank pte desc type calculate pte index click pte desc type href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l234 arch arm64 src common arm64 mmu c blank calculate pte index init xlat tables click calculate pte index href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l247 arch arm64 src common arm64 mmu c blank init xlat tables set pte block desc click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank init xlat tables calculate pte index click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l456 arch arm64 src common arm64 mmu c blank init xlat tables pte desc type click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank init xlat tables calculate pte index click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l472 arch arm64 src common arm64 mmu c blank calculate pte index pte desc type click calculate pte index href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l246 arch arm64 src common arm64 mmu c blank init xlat tables set pte block desc click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank init xlat tables pte desc type click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank calculate pte index pte desc type click calculate pte index href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l246 arch arm64 src common arm64 mmu c blank init xlat tables set pte block desc click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank init xlat tables set pte block desc click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank init xlat tables set pte block desc click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank init xlat tables set pte block desc click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank init xlat tables set pte block desc click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l447 arch arm64 src common arm64 mmu c blank init xlat tables setup page tables click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l456 arch arm64 src common arm64 mmu c blank pte desc type new prealloc table click pte desc type href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l234 arch arm64 src common arm64 mmu c blank init xlat tables setup page tables click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l456 arch arm64 src common arm64 mmu c blank init xlat tables new prealloc table click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l472 arch arm64 src common arm64 mmu c blank new prealloc table split pte block desc click new prealloc table href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l378 arch arm64 src common arm64 mmu c blank split pte block desc set pte table desc click split pte block desc href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l401 arch arm64 src common arm64 mmu c blank init xlat tables setup page tables click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l456 arch arm64 src common arm64 mmu c blank init xlat tables setup page tables click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l456 arch arm64 src common arm64 mmu c blank init xlat tables setup page tables click init xlat tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l456 arch arm64 src common arm64 mmu c blank setup page tables enable mmu el1 click setup page tables href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l515 arch arm64 src common arm64 mmu c blank enable mmu el1 arm64 isb click enable mmu el1 href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l532 arch arm64 src common arm64 mmu c blank arm64 isb enable mmu el1 click arm64 isb href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common barriers h l57 arch arm64 src common barriers h blank enable mmu el1 halt click enable mmu el1 href https github com apache nuttx blob 0f20888a0ececc5dc7419d57a01ac508ac3ace5b arch arm64 src common arm64 mmu c l542 arch arm64 src common arm64 mmu c blank we generated the call graph with this command bash cargo run grep call graph cut c 12 cut command removes columns 1 to 11 which produces this mermaid flowchart https mermaid js org syntax flowchart html text cargo run grep call graph cut c 12 flowchart td arm64 boot el1 init arm64 isb click arm64 boot el1 init href https github com apache nuttx blob master arch arm64 src common arm64 boot c l137 arch arm64 src common arm64 boot c arm64 isb arm64 boot el1 init click arm64 isb href https github com apache nuttx blob master arch arm64 src common barriers h l57 arch arm64 src common barriers h arm64 boot el1 init arm64 isb click arm64 boot el1 init href https github com apache nuttx blob master arch arm64 src common arm64 boot c l145 arch arm64 src common arm64 boot c arm64 isb arm64 boot el1 init setup page tables enable mmu el1 click setup page tables href https github com apache nuttx blob master arch arm64 src common arm64 mmu c l515 arch arm64 src common arm64 mmu c enable mmu el1 arm64 isb click enable mmu el1 href https github com apache nuttx blob master arch arm64 src common arm64 mmu c l532 arch arm64 src common arm64 mmu c arm64 isb enable mmu el1 click arm64 isb href https github com apache nuttx blob master arch arm64 src common barriers h l57 arch arm64 src common barriers h enable mmu el1 halt click enable mmu el1 href https github com apache nuttx blob master arch arm64 src common arm64 mmu c l542 arch arm64 src common arm64 mmu c source https gist github com lupyuen b0e4019801aaf9860bcb234c8a9c8584 the call graph is generated by our block execution hook like so https github com lupyuen pinephone emulator blob b23c1d251a7fb244f2e396419d12ab532deb3e6b src main rs l130 l159 call graph prints the call graph by looking up the block address in the elf context https github com lupyuen pinephone emulator blob b23c1d251a7fb244f2e396419d12ab532deb3e6b src main rs l224 l265 we map the block address to function name and source file in map address to function and map address to location https github com lupyuen pinephone emulator blob b23c1d251a7fb244f2e396419d12ab532deb3e6b src main rs l175 l222 elf context is explained here clickable call graph for apache nuttx real time operating system https lupyuen github io articles unicorn2 other emulators what about emulating popular operating systems linux macos windows android check out the qiling binary emulation framework qilingframework qiling https github com qilingframework qiling how about other hardware platforms stm32 blue pill and esp32 check out qemu stm32 blue pill unit testing with qemu blue pill emulator https lupyuen github io articles stm32 blue pill unit testing with qemu blue pill emulator nuttx on an emulated esp32 using qemu https medium com lucassvaz nuttx on an emulated esp32 using qemu 8d8d93d24c63 todo todo emulate input interupts from uart controller todo emulate apache nuttx nsh shell with uart controller todo select arm cortex a53 as cpu todo emulate multiple cpus todo emulate arm64 memory protection todo emulate arm64 generic interrupt controller version 2 todo read the symbol table in nuttx elf file to match the addresses received by block execution hook todo emulate pinephone s allwinner a64 display engine how to render the emulated graphics use web browser webassembly unicorn js will framebuffer emulation be slow | arm64 pinephone unicorn-emulator assembly emulator rust nuttx | os |
ESD---Lab-8 | esd lab 8 embedded systems design lab 8 design a high pass and low pass filter in vhdl | os |
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ml-from-scratch | machine learning from scratch this is the code repository for my machine learning from scratch youtube playlist https www youtube com watch v 4phi11lx11i list plp3anejkf1tzoz3hwoorclgrfvi8a76k2 01 linear regression using least squares check out the tutorial video https www youtube com watch v kr6tbaq16ng t 2s 02 linear regression using gradient descent check out the tutorial video https www youtube com watch v 4phi11lx11i t 2s check out the medium post https towardsdatascience com linear regression using gradient descent 97a6c8700931 03 linear regression in 2 minutes check out the medium post https towardsdatascience com linear regression in 6 lines of python 5e1d0cd05b8d | ai |
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soci_cdh_rtos | soci cdh rtos development of on board computer software on the nxp rt1020 board freertos based real time software runs the auto generated guidance and navigation gnc algorithms along with interfaces to all other subsystems sensors and actuators | os |
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shubham-koirala | shubham koirala information technology | server |
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moko-paging | moko paging img logo png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https img shields io maven central v dev icerock moko paging https repo1 maven org maven2 dev icerock moko paging kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name paging mobile kotlin paging this is a kotlin multiplatform library that contains pagination logic for kotlin multiplatform table of contents features features requirements requirements installation installation usage usage samples samples set up locally set up locally contributing contributing license license features pagination implements pagination logic for the data from abstract pagedlistdatasource managing a data loading process using pagination asynchronous functions loadfirstpage loadnextpage refresh or their duplicates with suspend modifier observing states of pagination using livedata from moko mvvm requirements gradle version 6 8 android api 16 ios version 11 0 installation root build gradle groovy allprojects repositories mavencentral project build gradle groovy dependencies commonmainapi dev icerock moko paging 0 7 1 usage you can use pagination in commonmain sourceset pagination creation kotlin val pagination pagination int pagination parentscope coroutinescope datasource lambdapagedlistdatasource currentlist extrenalrepository loadpage currentlist comparator comparator a int b int a b nextpagelistener result result list int if result issuccess println next page successful loaded else println next page loading failed refreshlistener result result list int if result issuccess println refresh successful else println refresh failed initvalue listof 1 2 3 managing data loading kotlin loading first page pagination loadfirstpage loading next page pagination loadnextpage refreshing pagnation pagination refresh setting new list pagination setdata itemslist observing pagination states kotlin observing the state of the pagination pagination state addobserver state resourcestate list itemclass throwable observing the next page loading process pagination nextpageloading addobserver isloading boolean observing the refresh process pagination refreshloading addobserver isrefreshing boolean samples please see more examples in the sample directory sample set up locally the paging directory paging contains the paging library the sample directory sample contains sample apps for android and ios plus the mpp library connected to the apps contributing all development both new features and bug fixes is performed in the develop branch this way master always contains the sources of the most recently released version please send prs with bug fixes to the develop branch documentation fixes in the markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master on release for more details on contributing please see the contributing guide contributing md license copyright 2020 icerock mag inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | moko kotlin-multiplatform kotlin-native ios android kotlin | front_end |
DeepLearning_ClinicalNotes | final project learning to diagnose using clinical notes video link https youtu be kv79w96w6w0 members jinmiao huang cesar osorio luke wicent sy environment setup local 1 conda env create f environment yml 1 install spark or download spark binary from here https spark apache org downloads html 1 pip install https dist apache org repos dist dev incubator toree 0 2 0 snapshots dev1 toree pip toree 0 2 0 dev1 tar gz the above command should install toree if it fails refer to github link https github com apache incubator toree note that toree was not included in environment yml because including it there didn t work for me before 1 jupyter toree install user spark home complete path spark 2 1 0 bin hadoop2 7 interpreters pyspark 1 extract the ff files to the directory code data diagnoses icd csv from bd4h piazza noteevents 2 csv cleaned version of noteevents csv luke s google drive https drive google com open id 0b7iqxokp3kpgwmfiuglnttbuwxm d icd diagnoses csv from bd4h piazza model word2vec v2 dim txt cesar s google drive https drive google com open id 0b5wtzczsz2x7evhankjonknwaws bio nlp vec pubmed shuffle win txt download here https github com cambridgeltl bionlp 2016 you will need to convert the bin files to txt i used gensim to do this model doc2vec v2 dim final csv cesar s google drive https drive google com open id 0b5wtzczsz2x7evhankjonknwaws 1 to run data preprocessing data statistics and ipynb related stuff start the jupyter notebook don t forget to set the kernel to toree pyspark jupyter notebook 1 to run the deep learning experiments follow the corresponding guide below environment setup azure 1 setup docker w gpu following this guide https github com nvidia nvidia docker wiki deploy on azure 1 using azure s portal select the vm s firewall in my case it showed azure01 firewall in all resources then allow port 22 ssh and 8888 jupyter for both inbound and outbound 1 you can ssh the vm through one of the ff docker machine ssh azure01 ssh docker user public ip addr 1 spark can be installed by following the instructions in environment setup local but note that this will not be as powerful as hdinsights i recommend taking advantage of the vm s large memory by setting the spark memory to a higher value spark folder conf spark defaults conf 1 if you have a jupyter notebook running in this vm you can access via http public ip addr 8888 1 to enable the gpus for deep learning follow the instructions in the tensorflow website link https www tensorflow org install install linux you can check the gpus status by nvidia smi folder structure code all source code data you should download and extract the data mentioned in environment setup local report latex source for this study s paper general pipeline 1 optional cleaned noteevents csv using postgresql imported noteevents csv by modifying mimic iii github https github com mit lcp mimic code and using the commands select regexp replace field e n r g the cleaned version noteevents 2 csv can be downloaded in the google drive mentioned in environment setup local 1 run preprocess ipynb to produce data hadm and data hadm cleaned 1 run describe icd9code ipynb and describe icd9category ipynb to produce the descriptive statistics 1 run feature extraction seq ipynb and feature extraction nonseq ipynb to produce the input features for the machine learning and deep learning classifiers 1 run ml baseline py to get the results for logistic regression and random forest 1 run nn baseline train py and nn baseline test py to get the results for feed forward neural network 1 run wordseq train py and wordseq test py to get the results for conv1d rnn lstm and gru refer to help or the guide below on training and testing for keras deep learning models training and testing for feed forward neural network prerequirest keras tensorflow or keras theano models are specified in nn baseline models py run nn baseline preprocessing to prapare the data for training and testing use training you can also run training with default arguments pythno nn baseline train py or run training script with customized input arguments python nn baseline train py epoch 10 batch size 128 model name nn model 1 pre train false please refer to parse args function in nn baseline train py for the full list of the input arguments testing test model with default model and data file python tfidf test py please refer to parse args function in nn baseline train py for the full list of the input arguments training and testing for recurrent and convolution neural network similar to feed forward neural network users can run the training and tesing with the default settings in wordseq train py and wordseq test py all the model architectures are specified in wordseq models py | ai |
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Amazfitbip-FreeRTOS | amazfitbip freertos warning this is not yet a complete replacement and was not tested on a real device it is a starting project to experiment on the platform at this stage the firmware must be loaded with an st link v2 connected directly to swdio and swclk you can find the location of these two pins in this picture https github com amazfitbip documentation blob master images amazfit bip top 00 png when loaded it should print hello world on the usart3 pc10 as tx and pc11 as rx also refer to the picture above i suggest to try this only on bricked devices if you have one to donate to this cause please contact me information on the hardware https github com amazfitbip documentation restore original firmware unbrick to revert to the original firmware just flash with st link the dump available from this page version 0 1 1 14 https myamazfit ru threads bip instrukcija po vosstanovleniju chasov raskirpichivaniju s ispolzovaniem programmatora st link v2 537 how to compile download the toolchain from here https developer arm com open source gnu toolchain gnu rm downloads extract the folder to your preferred location e g to your home folder then export path path home gcc arm none eabi 8 2018 q4 major bin remember to change the name accordingly now clone this repository and compile git clone recursive https github com dslul amazfitbip freertos cd amazfitbip freertos make recommended readings beginning stm32 developing with freertos libopencm3 and gcc by gay warren | os |
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